Here’s the coolest “technology-meets-ingenuity-meets-sustainable-economics” story that I’ve heard in a long time: the International Sun-Earth Explorer-3 (ISEE-3) Reboot Project, a crowd-funded rescue mission to repurpose a 36-year-old NASA spacecraft.

Operating out of an abandoned McDonald’s restaurant near NASA’s Ames Research Center in California, a team led by former NASA employee Keith Cowing and “space technologist” Dennis Wingo cut a deal with the U.S. space agency. The group would try to wake up the ISEE-3’s onboard systems, refire its engines, bring the craft back into Earth orbit, and put the ancient mariner to work on new tasks.

That’s probably the best use of an abandoned McDonald’s hamburger stand that I’ve heard. (Actually, it’s the only one I’ve heard of. Who could imagine a place and people without the need for a Big Mac?)

Perhaps the Golden Arches caught their attention, but in place of the iconic curved brandmark, these visionaries saw the arcs of possible trajectories, intersections in space and time.

In any case, after digging through old documents in the basements of retired NASA engineers, the reboot team recreated the command language and began executing their mission. To their surprise they discovered that many of the ISEE-3’s sensors were still operable after all these years and having absorbed five times their design level of space radiation. And there was even a little gas left in the tank!

Why did they do it, Cowing asked rhetorically in a July 19 New York Times op-ed article? “First, because we could: Recycling this piece of space hardware seemed cool and fun. And second, because it might generate useful scientific data — and we could take people all over the world along for the ride,” he answered.

For me, the storyline of the ISEE-3 Reboot Project resurrected not only an aging spacecraft, but the flagging narrative of American can-do, imagination, and energy — those natural dynamics of a young country.

But now this nation is middle-aged, has put on some pounds, been around the block more than a few times. Often it seems as if all that Yankee ingenuity has gone to figuring out ways to make as much cash as soon as possible with other people’s money.

So, this could be an ISEE-3 moment for the GPS program, with lessons that could be learned by all the world’s GNSS operators.

Any one who has had a bathroom or kitchen remodel project has come face to face with the unhappy reality that many things aren‘t built as good as they used to be or even available at any cost. Skill sets and supply lines have disappeared. Components are unreliable and fail quickly.

In many ways, legacy GPS space vehicles have demonstrated the durability of ISEE-3 — the latter voyage begun, by the way, the same year as the first GPS satellite was launched. The 2nd Space Operations Squadron recently removed SVN-34 from its primary orbital slot. Launched in October 1993, the satellite far exceeded its 7.5-year design life and remains capable of broadcasting healthy signals.

The far-reaching changes in stakeholder leadership described by Dee Ann Divis in this month’s Washington View column gives us the chance to think outside as well as inside the box. It provides an opportunity to build not merely personal resumes but to rebuild and build out an enterprise that has in its way transformed modern life as thoroughly as the Internet or mobile communications.

GPS is the best non-military deal this country has gotten since the National Defense Highway System. But just as the crisis in the Highway Trust Fund has shown, GPS could suffer the same fate of underinvestment — of ideas as well as money.

Despite our recent diminishing expectations for the mean mission duration of products like dishwashers and refrigerators, we need to treat GNSSs as long-term capital assets with a leadership perspective as strategic as the infrastructure and national role that it represents.

That may require us to look beyond American shores for solutions.

We need to recognize that the decisions we are making now are not for ourselves, or at least for ourselves alone, but for our children and our grandchildren. So, our mindset should not be that of quarterly profits and live for today (sha-na-na) but for a future world that will have ever-greater needs for affordable, available, and accurate positioning, navigation, and time.

The post The Next Big Mac appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

1996 soccer game in the Midwest, (Rick Dikeman image)

Nouméa ground station after the flood

A pencil and a coffee cup show the size of NASA’s teeny tiny PhoneSat

Bonus Hotspot: Naro Tartaruga AUV

Pacific lamprey spawning (photo by Jeremy Monroe, Fresh Waters Illustrated)

“Return of the Bucentaurn to the Molo on Ascension Day”, by (Giovanni Antonio Canal) Canaletto

The U.S. Naval Observatory Alternate Master Clock at 2nd Space Operations Squadron, Schriever AFB in Colorado. This photo was taken in January, 2006 during the addition of a leap second. The USNO master clocks control GPS timing. They are accurate to within one second every 20 million years (Satellites are so picky! Humans, on the other hand, just want to know if we’re too late for lunch) USAF photo by A1C Jason Ridder.

Detail of Compass/ BeiDou2 system diagram

Hotspot 6: Beluga A300 600ST

1. SMART BALL
Portland, Oregon USA
√ Adidas has designed every official World Cup ball since 1970. And that’s not all! The Adidas Innovation Team in Portland, Oregon, spent 4 years on a smart soccer ball with a “six-axis MEMS accelerometer sensor package” that can detect speed, spin, strike and flight path data and whip it on over to the special GPS app on your iPhone. The app interprets the data for you, coaches you, and keeps a video to show your friends. On sale now for only $299.

1. SMART BALL
Portland, Oregon USA
√ Adidas has designed every official World Cup ball since 1970. And that’s not all! The Adidas Innovation Team in Portland, Oregon, spent 4 years on a smart soccer ball with a “six-axis MEMS accelerometer sensor package” that can detect speed, spin, strike and flight path data and whip it on over to the special GPS app on your iPhone. The app interprets the data for you, coaches you, and keeps a video to show your friends. On sale now for only $299.

• (May 27, 2014)  Adidas launches smart ball

2. ROOM FOR MORE
Cape Canaveral, Guiana Space Center
√ In less than a decade, we’ll have 100+ GNSS satellites in orbit. The U.S. and the E.U. are doing their part with launches this summer. The seventh GPSII-F will go up from Cape Canaveral on July 31/August 1 and two Galileo FOC satellites will head for the skies from Kourou around August 22.

• GPS.gov Space Segment page and launch countdown
• European Space Agency News

3. LONGITUDE PRIZE II
Greenwich, United Kingdom
√ The Longitude Prize of 1714 was won in bits and pieces by John Harrison for his marine chronometer. Some 300 years later, the British Government decided to encourage solutions to 21st century problems using a renewed version of that drawn-out competition. This one is worth £10 million (US$17 million). The royal museums at Greenwich joined in with a new exhibit: Ships, Clocks & Stars, running through January 4, 2015, and featuring the original Longitude Act and Harrison’s five clocks.

• The New Longitude Prize

4. HAPPY ROUTES
Barcelona, Spain and Torino, Italy
√ Yahoo Labs in Spain and researchers in Turin came up with a mapping
algorithm to find the most “emotionally pleasant” ways to get around.
They used 3.7 million locations in London from Geograph and Street View,
then crowdsourced the response to each location using UrbanGems.org.
They plotted the most beautiful, quiet or happy routes and compared them
to the shortest. Guess what? It takes only 12% more time, on average,
to walk in beauty.

• (July 3, 2014) arXiv.org: The Shortest Path to Happiness

5. NATIONAL SECURITY
Beijing and Cupertino, California
√ China’s state broadcaster CCTV called the iPhone OS7 a national
security concern on July 11. They interviewed Ma Ding of People’s Public
Security University in Beijing, who said the  “frequent location”
function could provide Apple with confidential information about the
economy “or even state secrets.” The expensive iphone is popular among
high-level bureaucrats and business leaders. Apple said they have never
allowed access to their servers nor created a backdoor for any agency of
any government and “we never will.”

• (July 12, 2014) China Post: iPhone Called National Security Concern in CCTV Interview
• Ships, Clocks and Stars exhibit
• Apple location privacy statement

The post GNSS Hotspots | July 2014 appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

For at least two decades, GPS experts, geodesists, and public agencies have been working together to develop high-accuracy, large-scale continuously operating GPS reference stations that provide them the capability to monitor and model crustal deformation, tectonic plate movement, and the effects of geohazards such as earthquakes and volcanic eruptions.

Now, GNSS-augmented advance warning systems are going into place that can give us a crucial margin of safety in the event of an earthquake.

And none too soon.

For at least two decades, GPS experts, geodesists, and public agencies have been working together to develop high-accuracy, large-scale continuously operating GPS reference stations that provide them the capability to monitor and model crustal deformation, tectonic plate movement, and the effects of geohazards such as earthquakes and volcanic eruptions.

Now, GNSS-augmented advance warning systems are going into place that can give us a crucial margin of safety in the event of an earthquake.

And none too soon.

The latest Updated National Seismic Hazard Maps recently released by the U.S. Geological Survey (USGS) indicate a higher level of earthquake risk for the West Coast and some areas of the Midwest and East Coast then previously thought. (See the related news article in this issue on page 18.) In the next 30 years, the USGS says, California has a 99.7 percent chance of a magnitude 6.7 or larger earthquake, and the Pacific Northwest has a 10 percent chance of a magnitude 8 to 9 megathrust earthquake on the Cascadia subduction zone.

The Federal Emergency Management Agency (FEMA) has estimated the average annualized loss from earthquakes nationwide to be $5.3 billion. According to FEMA, 77 percent of that figure ($4.1 billion) comes from California, Washington, and Oregon, with 66 percent ($3.5 billion) from California alone.

So, an ongoing effort by the USGS and partner agencies and institutions to establish a West Coast Earthquake Early Warning (WC-EEW) system as the prototype for an eventual nationwide “ShakeAlert” system seems especially timely.
The early warning system exploits physical characteristics of earthquakes, which generate two main types of waves: rapidly moving primary or P-waves and the slower secondary (S) and surface waves that cause more intense and damaging ground shaking. (See accompanying figure.)

By detecting and analyzing the location and magnitude of an earthquake reflected in the P-wave energy, expected ground-shaking levels across a region can be estimated and warnings sent to local populations before more damaging shaking arrives with or after the S-wave. The advanced warning can range from seconds up to more than a minute, depending on the distance an affected area is from the earthquake’s origin.

Ken Hudnut, a geophysicist at the USGS Earthquake Science Center in Pasadena, California, and chair of the GNSS Working Group for the WC-EEW, has a long history in working in the area of geohazards. Dr. Hudnut received an A.B. degree in Earth sciences from Dartmouth College and a Ph.D. in geology from Columbia University. Before joining the USGS in 1992, he was a post-doctoral fellow at the California Institute of Technology Seismological Laboratory and currently is a visiting associate in geophysics on the faculty of the California Institute of Technology.

We called on Dr. Hudnut to discuss the state of the art in seismic science and the role of GNSS in that research and in the design and operation of earthquake early warning systems.

IGM: The USGS has a long history of developing instrumentation for the study of earthquakes and other types of Earth movement. What does GNSS positioning bring to the task that seismic sensors do not provide and, more specifically, how do GPS/GNSS data benefit EEW systems?

HUDNUT: GNSS positioning is especially good at rapidly giving us the change in a station’s position. Seismic sensors measure vibrations very well, but GNSS is better at measuring permanent displacement.
In a big earthquake, a station might move by several meters in several seconds, and not just in a simple straight line. The shaking may include erratic oscillatory displacements that are several times larger than the permanent displacement. Even though GNSS was never intended to measure such large, sudden, and jerky movements, we find that it works very well and provides a great augmentation to the seismic sensors that are currently in use for earthquake early warning.

IGM: How is GNSS data different from that obtained from these seismic sensors and how is it merged in an EEW system?

HUDNUT: GNSS data add to system robustness because they are an independent measurement. The seismic sensors are basically a mass on a spring, whereas GNSS is measuring position variation using changes in ranges to a constellation of satellites, so it’s a totally different kind of observation. Having both types of data makes the system stronger because we can immediately rule out glitches coming from one sensor type or the other. The diversity of observations gives us more strength.

As for merging the data, there is an abundance of literature and we are testing everything from uncoupled to loosely coupled and tightly coupled, and using a variety of methods. There are trade-offs in terms of simplicity, speed, and smoothing that we’re evaluating on an ongoing basis. We are creating a hair-triggered system that is also very robust even during large dynamic displacements, which is not a slam dunk. We’ve learned a lot by studying the methods of combining being done for strap-down avionics, that is, navigation and positioning systems, and looking at both commercial off-the-shelf solutions and open-source options.

IGM: What GPS/GNSS signals, satellite observables, and signal components (e.g., code vs carrier phase) are used in the EEW system?

HUDNUT: Right now, we are mostly reliant on GPS alone, but we have upgraded to GNSS receivers at our stations over the past several years. Of course we’re doing phase-differential, dual-frequency processing to get the few-centimeter accuracy in real-time; so, we do widelaning and narrowlaning, but code is relatively unimportant to us — we rely heavily on the carrier phase. We’re using precise point positioning with ambiguity resolution, which is possible for GPS these days and in the future may be possible for GLONASS as well. Our limited telemetry bandwidth doesn’t allow us to bring back all of the GLONASS and other GNSS data just yet.

IGM: What practical benefits are provided by an impending seismic movement alert on the order of tens of seconds?

HUDNUT: Applications envisioned are getting school children to safety under their desks that much sooner, and operating automatic shut-off valves, putting computer systems into a safer state, or switching other automated systems to try to prevent loss of life or damage to property. If you were having surgery performed at that time, wouldn’t you want the surgeon to remove the scalpel to safety right before the shaking started?

We want to make it possible for people to invent their own applications, and we expect this to happen here as it has in Japan, Mexico, and other countries that have already had EEW for many years and even decades. In Japan, EEW protects the Shinkansen (bullet train) system. In California, BART is testing use of EEW and figures it could help prevent or lessen the severity of future derailments.

IGM: In recent years, a number of demonstration campaigns have been conducted, involving public agencies and citizen participants in sending and receiving test notifications of an earthquake. What have been some the most important lessons learned from those campaigns?

HUDNUT: ShakeOut is our annual public drill to encourage “Drop, Cover, and Hold On” by everybody. We started this in 2008 in California and it has grown worldwide. We use that as an earthquake hazard awareness opportunity for publicity for EEW. In general, ShakeOut encourages a personal action that could be done even quicker if one had an operational public EEW.

With the ShakeAlert EEW system, what we have been doing for the past couple of years is a slow roll-out through selected “beta-users.” We don’t want to roll this out to the public before it’s ready because of the “cry wolf” gotcha. Most county-level and large cities’ emergency operation centers, plus Caltrans and BART for example, have the ShakeAlert UserDisplay installed so that they could potentially relay an alert through dispatch communications systems.

The post GNSS & Geohazards appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

New operatives and appointees flock to the centers of power in the early days of each administration and the opening of each Congress, then migrate to friendlier climes as congressional elections loom and the administration winds down — as it is now.

Washington, D.C., has a peculiarity of seasons. While most of the world marks the shifts between winter and spring, summer and autumn, the politicos on the streets of the U.S. capital count the passage of time in two-year increments.

New operatives and appointees flock to the centers of power in the early days of each administration and the opening of each Congress, then migrate to friendlier climes as congressional elections loom and the administration winds down — as it is now.

The GPS community, in particular, seems to be caught up in this season’s migration. New personnel are stepping into key leadership posts in the departments of defense and transportation as well as other agencies that work together to guide the GPS program.

The National Coordination Office (NCO) for Space-Based Positioning, Navigation, and Timing (PNT), which supports all of these efforts, has been operating for months without a top executive. Changes are occurring in the congressional committees that authorize the GPS budget and perhaps in those that can influence the fortunes of the constellation’s operations and utility.

These personnel shifts are occurring just as a new effort to rewrite the nation’s telecommunications laws is emerging, potentially threatening GPS frequencies and applications.

The most notable shifts are within the Department of Defense (DoD). General William Shelton, who became Commander of Air Force Space Command (AFSPC) in January 2011, will retire September 1. By all accounts his leadership of those responsible for the modernization and support of the GPS system has been admirable. Although no job at this level is ever easy, his tenure has weathered one squall after another with years of unusually serious budgetary challenges, glitches in efforts to enhance the system, and the rise of new threats to, and doubts about, the availability of GPS signals.

When he assumed the helm in 2011, Shelton stepped immediately into the middle of what would become the biggest threat so far to GPS frequencies. That same month a Virginia firm called LightSquared, amid great controversy, won conditional permission from the Federal Communications Commission (FCC) to build a coast-to-coast, high-powered terrestrial broadband network using frequencies neighboring those used by GPS. The firm’s project fit neatly into the Obama administration’s plan to provide more spectrum for broadband companies and encourage competition within the wireless communications industry.

RF power from LightSquared’s transmitters and mobile devices, however, threatened to overwhelm GPS receivers across the country. Shelton took a stand against LightSquared’s aspirations and made national news for refusing to submit to pressure from the White House to soften congressional testimony about the clear interference problems that testing had shown. Although the issue is not fully resolved, the Light-Squared project is currently sidelined.

Patent, Jamming, and Budget Problems
Other challenges followed, including an effort by the British Ministry of Defense to patent one of the modernized GPS signal structures — a patent that was withdrawn after what experts described as government-to-government discussions. Other concerns arose about the aging GPS constellation, repeated demonstrations that the GPS signal could be spoofed and jammed, and surging threats from hackers.

Shelton also found himself caught up in budget battles consuming Washington as well as significant problems with the navigation payload on the new GPS III satellites and the new ground system. To tackle both the technical and financial problems, his team weighed a variety of alternatives including reconfiguring the GPS constellation to take advantage of very small satellites and investing in the ability to launch more than one satellite at a time. Among the outcomes of that work is the recent restructuring of the ground system contract and the release in June of a request that could lead to a new prime contractor for the last 22 GPS III spacecraft.

Shelton seized some opportunities as well. He is credited with turning on the navigation message in the L2C and L5 signals, a move expected to spur receiver development and innovation as the commercial sector gains experience with the new signals.

New AF Space Commander
Shelton will be replaced by Lt. Gen. John Hyten, who has served as his AFSPC vice-commander for the last two years. Hyten has a Harvard engineering degree and a masters in business administration and has served in Washington as director for space programs in the Office of the Assistant Secretary of the Air Force for Acquisition — experience that should serve him well has as sequestration reemerges and squeezes the GPS budget tighter still.

Hyten’s experience as the director of space forces during Operations Enduring Freedom and Iraqi Freedom, and in senior engineering positions on anti-satellite weapon system programs, also could prove very valuable as the challenges from adversaries mount. He also has worked with the GPS program before, noted one source, and has direct experience with the interagency process that guides GPS policy.

“He’s definitely one of the GPS people,” said the well-informed source, who asked not to be identified due to a lack of authorization to speak publicly. “We’re glad to have him over in Space Command.”

In yet another change for Space Command, Lt. Gen. Ellen Pawlikowski, the head of the Space and Missile Systems Center at Los Angeles Air Force Base, has left to become military deputy within the Office of the Assistant Secretary of the Air Force for Acquisition — replacing Lt. Gen. Charles Davis as the service’s top military acquisition official. SMC, which is part of Space Command, is home to the Global Positioning Systems Directorate.

Taking over at SMC is Lt. Gen. Samuel Greaves, who was serving as deputy director in the Missile Defense Agency’s Office of the Undersecretary of Defense for Acquisition, Technology and Logistics.

Other PNT Leadership Changes
The handoffs within Space Command are not the only changes in the GPS management team at the Department of Defense.

Robert Work has replaced Ashton Carter as the deputy secretary of defense and takes his place as co-chair of the National Executive Committee for Space-Based Positioning, Navigation, and Timing (PNT ExCom). Work, who served 27 years in the U.S. Marines, was undersecretary of the Navy from 2009 to 2013. He has a master of science degree in space system operations and served on the DoD transition team for the in-coming Obama administration. He was confirmed as deputy secretary in April.

His co-chair on the PNT ExCom is also relatively new to the job. Victor Mendez is acting deputy secretary of transportation. He stepped into the roll in late December 2013 to replace the departing John Porcari. Mendez, who has an MBA and a degree in civil engineering, was sworn in as the federal highway administrator in July 2009. He also served on the White House transition team.

The PNT ExCom is supported by the Executive Steering Group, which meets more often than the ExCom, and deals with interagency matters that can be handled without elevating them to the deputy secretary level. The Steering Group has representatives from the same departments as the ExCom plus agencies within those departments such as the Air Force and the Federation Aviation Administration.

The Pentagon’s chief information officer (CIO) and the Department of Transportation (DoT) assistant secretary for research and technology co-chair the Steering Group. Greg Winfree has held the latter position for a while, but the DoD CIO, whose office is the nexus for DoD decisions on GPS policy and procurement, is new. Teri Takai left that job at the beginning of May with only a week’s notice to the surprise of many in the GPS community

“I think she did a lot to establish the influence of the CIO office,” said Scott Burgett, director of automotive OEM platform engineering for GPS user equipment manufacturer Garmin International, who praised her as “a very effective advocate for GPS.”

Takai has been replaced, for now, by Terry Halvorsen, who is the Pentagon’s acting CIO. His role, however, may not be permanent. According to Defense Daily, the DoD has issued a formal statement from spokeswoman Lt. Col. Valerie Henderson stating, “Mr. Halvorsen will lead the DoD CIO organization until a permanent DoD CIO is selected by [Defense] Secretary [Chuck] Hagel.”

Halvorsen’s situation underscores the challenges facing anyone in a job in an acting capacity. When so much depends on the skills, expertise, perspectives, and advocacy of individuals, having people who may not be staying — or are perceived as temporary — makes planning and progress much harder.

ExCom Newbies
The departments of state, commerce and homeland security all have representatives to both the ExCom and the steering group who are either new to their jobs or still in an acting capacity.

Bruce Andrews was just named acting deputy secretary of commerce in June. His experience in telecommunications could be especially useful as the battle over spectrum heats up. He was general counsel for the Senate Committee on Commerce, Science, and Transportation, the committee that oversees the FCC. Prior to that he was a telecommunications attorney and managed government affairs for Ford Motor Company.

Commerce is represented in the PNT Executive Steering Group by former astronaut Kathryn Sullivan, who was finally confirmed in March as the under secretary of commerce for oceans and atmosphere and as National Oceanic and Atmospheric Administration (NOAA) administrator. She is an oceanographer who, when she served as NOAA’s chief scientist, oversaw a research and technology portfolio that included fisheries biology, climate change, satellite instrumentation, and marine biodiversity.

The GPS commercial sector may particularly appreciate the background of Judith Garber, the State Department’s ExCom representative and the acting assistant secretary of state for the Bureau of Oceans and International Environmental and Scientific Affairs (OES). She is a career foreign service officer who has held economic and business development posts around the world and was named to her new post at the end of April.

The State Department’s person in the steering group is Jonathan Margolis, acting deputy assistant secretary at OES for the science, space, and health. He has a Ph.D. in psychology from Harvard University, focusing on negotiation and conflict resolution, and a master’s degree from the Fletcher School of International Law and Diplomacy as well as hands-on experience organizing international communications. Given that the current round of international negotiations over spectrum is coming to a head next year, his skills could be very valuable.

Just joining the PNT ExCom is Deputy Secretary of Homeland Security (DHS) Alejandro Mayorkas. A former U.S. attorney, he had been serving as the director of DHS’s United States Citizenship and Immigration Services (USCIS), which operates the largest immigration system in the world. He was sworn in last December.

Confirmed in March, as under secretary for DHS’s National Protection and Programs Directorate, Suzanne Spaulding brings a wide range of experience in intelligence and cybersecurity matters. She served at the Central Intelligence Agency for six years and on the staff of both the House and Senate intelligence committees.

Spaulding’s role at DHS gives her the responsibility for protecting critical infrastructure. GPS already is considered an essential element in most of the nation’s critical infrastructure sectors, and some top experts have argued GPS should itself be designated critical infrastructure.

New Leadership at NCO
The leadership at the National Coordination Office, the permanent staff for PNT ExCom, is also in flux. Former NCO director Jan Brecht-Clark retired in December after serving just a year. The NCO’s deputy director, Col. Harold “Stormy” Martin, retired from military service several months later.

Now that Martin has left the military he could potentially return as the NCO’s director — the path taken by Anthony Russo, who was NCO director from January 2010 through December 2012. The DoT selects the NCO director and is expected to name its choice in early July, although nothing had been announced as of press time.

Meanwhile, on Capitol Hill
Key personnel changes are also under way in Congress with some members retiring, some moving to different roles because of term limits, and others forced out by election losses.

Among those retiring will be the chairmen of the both the House and Senate armed services committees, which oversee the GPS program. Overall, these committees and their chairmen have been very supportive of GPS, particularly in their budget authorizations.

House Armed Services Chairman Howard “Buck” McKeon, R-California, who is retiring from Congress, told reporters that term limits, which mandate his giving up his chairmanship, were the “biggest motivator” for his decision. Rep. Mac Thornberry of Texas, who is described in news reports as being extremely knowledgeable on defense matters and who has already made his interest in the chairmanship clear, is the most likely replacement.

Sen. Carl Levin, D-Michigan, chair of the Senate Armed Services Committee, is also retiring. He has endorsed Sen. Jack Reed, D-Rhode Island, as his successor, according to the web site Daily Kos, but Reed is also in line to take the soon-to-be-vacant chairmanship of the Senate Banking, Housing and Urban Affairs Committee. Senators Bob Nelson, D-Florida and Claire McCaskill, D-Missouri, would be next in line by seniority although Nelson has a number of other options.

The leadership situation in two other Senate committees is particularly worth watching. Sen. John D. Rockefeller, D-West Virginia, who now heads the Commerce and Transportation Committee, is retiring after five terms. Mark Pryor, D-Arkansas, the chairman of the Communications, Technology and Internet Subcommittee, is in a very close race this fall. These two committees have the lead on spectrum issues in the Senate and played a role in the LightSquared debate a few years ago.

The chairmanships are particularly important in this case because the heads of the corresponding House committees have launched an effort to update the Communications Act of 1934 and have placed issues that could greatly impact GPS squarely on the table.

House Energy and Commerce Committee Chairman Fred Upton, R-Michigan, and Communications and Technology Subcommittee Chairman Greg Walden, R-Oregon, announced their multi-year review in December. They have asked for feedback on issues including receiver standards and the idea of altering the role the National Telecommunications and Information Administration (NTIA).

NTIA watches out for the federal government users of frequencies — including those who rely on GPS — and played a key role in protecting the GPS spectrum during the LightSquared controversy. As things now stand, the FCC and NTIA have to agree on frequency allocations, an arrangement deemed duplicative by some who would like the FCC to have most if not all of the decision-making power. For more on this story, see news article on page 20.

Upton and Walden appear well positioned and, given that term limits will force Upton to relinquish his chairmanship by 2017, well motivated to launch legislation next session. Whether they succeed or not depends in part on who chairs the Senate committees. Sen. Barbara Boxer, D-California, is next in line for the chairmanship, but she already leads other committees and the Democrats have not imposed term limits on their members. Democrats Bill Nelson of Florida and Maria Cantwell of Washington state would seem to be likely choices based on seniority.

In any case, it’s too soon to know, particularly since most political experts give the Republicans better than even odds of taking control of the Senate in this fall’s elections. If that happens, then Republicans will control the chairmanships and set the agenda. If they can come to agreement amongst themselves, they will be in a much stronger position to push changes through.

Unfortunately, the GPS community already has lost some of the members who acted to protect GPS frequencies during the LightSquared fracas.

Of the six members that organized “Dear Colleague” letters opposing LightSquared‘s request in the spring of 2011, half are gone or on their way out. Sen. Ben Nelson, D-Nebraska, and Rep. Steve Austria, R-Ohio, both declined to seek re-election in 2012. Rep. Ralph Hall, R-Texas, the oldest-serving member of Congress, lost his primary bid to a Tea Party challenger this spring. A fourth GPS advocate, Rep. Collin Peterson, D-Minnesota, is more likely than not to win, according to Larry Sabato, an expert on electoral politics at the University of Virginia’s Center for Politics, but he is in a competitive race.

All in all, the GPS community is facing a substantial new challenge over spectrum with a team that is largely new to GPS issues. It also has fewer proven friends on Capitol Hill to speak on its behalf. The good news is that the new contingent of GPS leaders has an array of particularly useful skills and time to plan ahead for the next fight. Whether they will be ready or not remains to be seen.

The post New Leaders at the GPS Helm appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Working Papers explore the technical and scientific themes that underpin GNSS programs and applications. This regular column is coordinated by Prof. Dr.-Ing. Günter Hein, head of Europe’s Galileo Operations and Evolution.

Working Papers explore the technical and scientific themes that underpin GNSS programs and applications. This regular column is coordinated by Prof. Dr.-Ing. Günter Hein, head of Europe’s Galileo Operations and Evolution.

In GNSS receivers, the acquisition process is the first stage of the signal-processing module. It consists in assessing the presence of GNSS signals and providing a rough estimation of the incoming signal parameters: the Doppler frequency and the code delay.

To detect the presence of the signal, the received signal is correlated with a succession of locally generated replicas until the acquisition detector crosses a predefined threshold. One commonly used criterion of acquisition performance is the probability of detection when the parameters of the local replica are (close to being) correct. This probability should be as high as possible but under unfavorable conditions, such as adverse environments, detection becomes a challenge.

Initially, GNSS signals were only defined on one component (such as GPS L1 C/A) but the new generation of signals has two components (such as GPS L1C, GPS L5, Galileo E1 OS, Galileo E5 a/b, and so forth): a data component that carries the navigation message and a pilot component, which does not carry any useful information.

Designers of the modern civil signals introduced the pilot component in order to avoid the data bit transition problem during the tracking process. From the point of view of signal acquisition, however, the presence of a systematically known secondary code on the pilot component still implies bit sign transition. The presence of the pilot signal also means that the total signal power is split between components, thus impacting the way to process such a signal to gather all the signal power.

The objective of this article is to study the typical sources of performance degradations of the GNSS acquisition process that are generally overlooked in the literature and to assess their effects on the acquisition of new GNSS civil signals. We will focus on degradations due to (1) the uncertainties brought by the choice of the acquisition grid, (2) the presence of bit sign transition, and (3) the non-compensation of the code Doppler. Further to the pure acquisition performance, we also analyze the acquisition of the secondary code for new GNSS signals and the frequency refinement because these factors are necessary conditions with which to initiate standard tracking.

This study takes place in the context of the development of a GNSS software receiver that aims at acquiring any GNSS civil signals at 27 dB-Hz and higher with a strong probability of detection set to 95 percent. As a consequence, all presented results refer to this test case.

In this article, we will first introduce the required acquisition parameters to achieve the 27 dB-Hz/95 percent objective without considering any aforementioned source of degradation. Then, we discuss each point of degradation independently and analyze its effect on the probability of detection.

GNSS Signals
In this article, we consider the civil GPS and Galileo signals in the L1/E1 and L5/E5 bands.
The main points of design of a GNSS signal are:

• the carrier frequency f_L
• the spreading codes c₁ characterized by their length N_c1, its chipping rate f_c1, or equivalently its chip duration T_c1 = 1/f_c1 
• the spreading code chip modulation 
• the navigation message d on the data component and the secondary code c₂ on the pilot component (and sometimes also on the data component).

Table 1 summarizes the main signal features for the considered GNSS signals.

The down-converted and filtered composite GNSS signal entering the correlation block of the receiver can be generically represented as follows:

s(t) =  A_Dd(t – τ)C_2,D(t – τ)c_1,D(t – τ)p_D(t – τ) cos(2π(f_IF + f_d)t + φ_0,D) + A_pc_2,P(t – τ)C_1,P(t – τ)p_P(t – τ) sin(2π(f_IF +f_d)t + φ_0,P) + n(t)                     (1)

where

• x stands for “D” for the data component and “P” for the pilot component
• A_x is the signal amplitude on the component and depends upon the total signal power C
• p_x is the subcarrier modulating the spreading codes 
• τ is the receiver PRN code delay 
• f_1F is the received intermediate frequency of the receiver
• f_d is the incoming Doppler frequency
• φ_0,x is the initial phase on each component depending on the initial phase of the incoming signal
• n is the incoming noise, which is assumed to be a white noise with centered Gaussian distribution and a constant two-sided power spectral density equal to N₀/2 dBW-Hz.

Note that in this expression, the role of the RF front-end equivalent filter is purposely ignored for simplification reasons.

To complete the generic expression of the received GNSS signal (1), Table 2 provides the value for each parameter. As can be seen, the GNSS L5 signals are in quadrature; however, the phase relationship between the two components of GPS L1C is not yet specified. (For details, see the article by J. W. Betz et alia listed in Additional Resources section near the end of this article.)

For the purposes of this article, we designated L1C to be an in-phase signal as is the case for Galileo E1 OS. GPS L1C presents a power difference in both components — 75 percent of the power in the pilot component and 25 percent of power in the data component — whereas the total signal power is split in half 50/50 for the other GNSS composite signals.

GNSS Acquisition Performance in Ideal Case
This section presents the acquisition process in the case when none of the sources of error mentioned in the introduction are considered which can be found in many assessment articles in the literature. As explained in the introduction, the chosen test case is to acquire any GNSS civil signals at 27 dB-Hz (total signal carrier-to-noise-density ratio or C/N₀) with a probability of detection of 95 percent.

Correlation Operation. Considering the correlation operation for one component of the GNSS signal and assuming that:

• there is no data bit sign transition during the correlation process
• the correlation operation lasts for T_I seconds
• the parameters of the processed signal and the local replica are constant during the correlation operation such that the code delay error ε_τ and the Doppler frequency error ε_f are constant and the carrier phase error at the beginning of the correlation process is ε_Φ0.

The in-phase and quadrature-phase correlator outputs can be modelled as:

Equation (3) (see inset photo, above right, for all equations)

Note that the aforementioned correlator outputs model neglects the cross-correlation between the data and pilot component because the spreading codes were chosen to be as orthogonal as possible. Note also that the local spreading code is assumed to have the same modulation as the spreading code of the received signal.

Acquisition detector. A receiver can acquire composite GNSS signals by using correlator outputs based on one of the two components (in general, the pilot component) or both data and pilot components. In either case, the acquisition detector is defined as the sum of the squared correlator outputs (2 when only one component is used, 4 when two components are used). The acquisition detector for one component is thus

Equation (4)

where K represents the number of non-coherent summations. In this case KTI is referred to as dwell time, and the parameters of the local replica (local PRN code delay ̂τ and Doppler ̂f_d) are constant for the K correlations.

The acquisition detector based on the use of two components can be easily derived accordingly.

Probability of detection. The basic principle of acquisition is to sequentially compute the acquisition detector for all possible values of local code delay and local Doppler until the detector crosses a predefined threshold T_h. The set of the tested couples (̂τ, ̂f_d)
is defined as the acquisition matrix, and its size depends upon the uncertainty on the incoming signal code delay and Doppler frequency and on the sampling of these uncertainties.

The tested values of the acquisition matrix are referred to as acquisition bins, and the distance between two consecutive tested values is referred to as bin size. The detection performance of such a detector is generally computed based on a hypothesis test for each visited acquisition matrix bin: hypothesis H₀ assumes that the desired signal is not present and is tested against hypothesis H₁ that assumes that it is present.

Under hypothesis H₀ the correlator outputs only consist of independent Gaussian noises. In this case, the (normalized) detector follows a centered χ² distribution with 2K or 4K degrees of freedom for the one-component and two-component cases, respectively. For a desired probability of false alarm P_fa, we can thus define the appropriate threshold T_h.

The alternative hypothesis H₁ assumes that the signal is present, meaning that the parameters of the local replica are almost aligned with the ones of the received signal. In this case, the (normalized) detector follows a non-central χ² distribution with 2K or 4K degrees of freedom for one-component and two-component cases, respectively.

The non-central parameter of the χ² distribution depends upon the receiver signal C/N₀, the correlation duration T_I, and the uncertainty of the parameters (̂τ, ̂f_d)
due to the acquisition bin size. We can then compute the probability of detection P_d by comparing the detector distribution to the threshold T_h. As a synthesis, the key acquisition parameters are presented in Table 3.

Minimum Dwell Time to Reach a Desired Probability of Detection. To find results related to our test case, we selected a desired probability of false alarm P_fa = 1e^–3 as described in the RTCA, Inc. article referenced in Additional Resources. To reach this objective, determining the dwell time KxT_I is important. As T_I is generally taken equal to the spreading code period during the acquisition process, K is the critical parameter to play with.

Assuming that the acquisition bin size is infinitely small (thus meaning that ε_τ = ε_f = 0), Table 4 indicates the value of K to reach the proposed objective. This table shows that the composite GNSS signals having a data/pilot power share of 50/50 percent require a dwell time twice as short when both components are used compared to when only one component is used.

In the table, note that for GPS L1C, with a data/pilot power share of 25/75 percent, using only the pilot component or both components produces equivalent results. Finally, the well-known preferability of having a long coherent integration time to improve the acquisition detection performance explains why, for example, the GPS L1C and Galileo E1 OS require a lower dwell time than GPS L1 C/A or GPS L5. (See the discussion in F. Bastide et alia cited in Additional Resources.)

Effect of Acquisition Bin Size on Acquisition Detection Performance
Clearly, it is irrelevant to assume that the acquisition bin size is infinitely small. Indeed, a trade-off should be chosen between the acquisition bin size and the acquisition duration: a large bin size leads to degradation of the acquisition performance (the error between the tested values and the true values can be significant), while a narrow bin size means a significant number of bins potentially have to be visited, thus increasing the mean-time-to-acquire the signal.

In general, the acquisition grid is defined as a function of the maximum acceptable degradation on the detector. Following the example used in the RTCA/DO-235B, we chose

• a Doppler bin size of 1/2T_I, corresponding to an equivalent degradation of the received signal C/N₀ of 0.9 dB, which corresponds to a maximum Doppler frequency error |ε_f| ≤ 1/4T_I
• a bin size in the code delay domain sufficient to generate a maximum equivalent degradation of the received signal C/N₀ of 2.5 decibels. The code delay bin size thus depends on the autocorrelation function shape (and in fact on the RF front-end filter as well). For example, it corresponds to a bin size of one-half chip for an unfiltered GPS L1 C/A or GPS L5 signal.

Figure 1 shows the probability of detection as a function of the Doppler uncertainty created by the bin size for the selected test case and the number of non-coherent summations given by Table 4. In the worst case (limit of the cell), the probability of detection falls from 0.95 down to 0.8. A more relevant figure is the average probability of detection over the bin, assuming that the actual Doppler error is a random variable uniformly distributed over the entire bin. The average probability of detection is also plotted in Figure 1 and equals 0.91.

Figure 2 shows the same thing for the code delay uncertainty within one acquisition bin. In the worst location (on the edge of a bin in the acquisition grid), it goes down from 0.95 to 0.43 while the average probability of detection over the bin is 0.74.

If the worst cases in the frequency and time domains are combined, the total loss on the equivalent received C/N₀ is 3.4 decibels and results in a probability of detection down to 0.25 instead of 0.95. The average probability of detection over the bin is around 0.67, thus showing a theoretical degradation of performance of 28 percent.

Bit Sign Transitions and Receiver Performance
The presence of bit sign transitions affects receiver performance in signal acquisition detection. The following discussion addresses this phenomenon and associated factors.

Bit Transition Problem. The correlator output models provided in Equation (3) assumed that the data and/or the secondary code bits are constant during the correlation interval. During the acquisition process, however, we have no reason to assume that the integration interval is aligned with the data bit. Although often neglected in the literature, it thus seems necessary to develop the correlation output model consider-ing bit sign transitions. The authors have performed such a study, including the theoretical aspects for single- and dual-component signals, and a paper — M. Foucras et alia (2014a) in Additional Resources — describing the results will be submitted for publication. The following is a short summary with corre-sponding results.

The presence of a bit sign transition during the correlation operation degrades the useful part of the correlator output without modifying the power of the noise. This results in a degradation of the acquisition detector amplitude, the nature of which will depend upon the location of the bit sign transition in the integration interval, the number of non-coherent summations, and the Doppler frequency error ε_f as described in the paper by C. O’Driscoll.

In particular, the expression of the non-centrality parameter in case of a bit sign transition during the integration interval is given in M. Foucras et alia (2014a). As might be expected, the worst case is for a bit sign transition occurring in the middle of the correlation interval.

For all GNSS signals discussed in this article except GPS L1 C/A, a bit sign transition can occur at each spreading code period. This means that the correlation duration should be limited to the code duration, and that even then, a bit sign transition can potentially degrade all correlator outputs. In the article by M. Foucras et alia (2014b), the authors have identified for each GNSS signal the resulting average probability of detection for the number of bit sign transitions, taking into account the probability of occurrence.

In contrast, the acquisition performance of the GPS L1 C/A signal, when considering bit sign transition, depends on the correlation duration. Indeed, because the data bit duration is 20 times longer than the spreading code period, we can use correlation durations of 1, 2, 4, 5, 10 or 20 milliseconds. Each case will have a different probability of undergoing a sign transition during the correlation. Consequently, for an equivalent dwell time — say, 20 milliseconds — the effect on the acquisition performance depends on the choice of T_I as explained in M. Foucras et alia (2014b).

As shown in Figure 3, when the T_I is too short, the effect of the bit sign transition is slight, but it does not allow optimal detection. On the contrary, for long T_I, the effect of the bit sign transition is significant. Based on Figure 3, it appears that a correlation duration of 4 to 10 milliseconds is optimal to have the lowest dwell time to reach a probability of detection of 95 percent.

Resulting Probability of Detection. Table 4 provided the required dwell time to reach a probability of detection of 95 percent for a signal with a C/N₀ of 27 dB-Hz without considering bit sign transition or uncertainty due to the acquisition bin size. For the same dwell time and C/N₀, Table 5 shows the average probability of detection based on Monte Carlo simulations assuming that the distribution of the location of bit sign transition is uniform within the correlation interval (assumed equal to one spreading code). As discussed previously, for GPS L1 C/A we chose the coherent integration time to be the spreading code period (one millisecond). Note that in this latter case — considering the bit sign transition, Figure 3 showed that this value for T_I is not the optimal one.

Table 5 shows that all the composite signals are highly affected, mostly due to the fact that the correlation duration has to be chosen equal to the data bit/secondary code bit duration. As a consequence, it seems necessary for these signals to use techniques that are insensitive to data bit sign transitions, such as the techniques described in the article by M. Foucras et alia (2012). These techniques are generally more demanding in terms of resources. However, GPS L1 C/A is almost not affected thanks to its structure based on a data bit duration 20 times longer than the spreading code duration.

Uncompensated Code Doppler and Receiver Performance
We now turn to the question of the effect of an uncompensated code Doppler on acquisition detection performance.

Code Doppler problem
The Doppler frequency, mainly caused by the satellite motion and the receiver local oscillator, affects the processed signal by modifying

• the central carrier frequency — a change estimated by the acquisition process
• the code frequency (chipping rate) resulting in a code Doppler f_cd which depends on the incoming Doppler frequency f_d, the carrier frequency f_L and the chipping rate frequency f_c1 according to

Equation (5)

The modification of the code frequency leads to a change in the spreading code period as can be seen in Figure 4 where three periods of a four-chip spreading code are represented:

• A positive code Doppler frequency causes the spreading code duration to shrink (T_cd < T_c1).
• A negative Doppler shift causes the spreading code duration to expand (T_cd < T_c1).

The problem of the presence of an uncompensated code Doppler resulting in a difference between the code frequency of the received and the local signals for GPS L1 C/A has been addressed by several authors. E. D. Kaplan and C. Hegarty. (See Additional Resources). Foucras et alia (2014c) showed that the degradations due to uncompensated code Doppler are even more significant for the new generation of GNSS signals (higher code frequency, lower L-band central frequency, BOC modulation).

Table 6 presents the offsets between the local and received spreading codes after the dwell time for the signals being considered in this article. For GPS L1 C/A, GPS L1C, and Galileo E1 OS, the offset is lower than one chip even for high incoming Doppler frequency. Still, the offset can sometimes be greater than one code delay bin size, which can be problematic. For L5 signals, the offset exceeds one chip for an incoming Doppler of several hundreds of hertz with the considered dwell time. For high Doppler frequencies this means that the offset is too high to provide correct acquisition performance, as it will be shown later.

To illustrate this point, Figure 5, Figure 6, Figure 7 and Figure 8 represent the deformation of the squared correlation functions between the incoming signal spreading code and the local replica spreading code for GPS L1 C/A, GPS L5, Galileo E1 OS and GPS L1C, respectively, due to uncompensated code Doppler and with the dwell times as defined in Table 4.

For BPSK-modulated signals (GPS L1 C/A and GPS L5), the shape of the autocorrelation function becomes rounded and offset compared to the reference triangular curve. The amplitude of the maximum value of the correlation function is also reduced, and the peak is shifted to the right for a negative Doppler. The result is a degradation of the probability of detection and a potential missed detection due to the motion of the correlation peak with time.

Even if the correlation function–peak offset is not such a problem for GPS L1 C/A due to its relatively slow chipping rate, this can be a real problem for GPS L5, as seen in Figure 6 where the correlation peak has moved by more than one chip over the 217-millisecond dwell time. For Galileo E1 OS, the CBOC modulation’s correlation function has a significantly reduced amplitude and its shape becomes flat when the code Doppler increases due to the presence of the side peaks. This can then create a detection problem as several bins could trigger a detection.

If the slip between the received and the local spreading codes exceeds one chip, then the correlation process no longer makes sense because the power of the signal cannot be accumulated since the correlator output is essentially noise. Figure 9 shows the linear relationship between the incoming Doppler frequency and the time to the slip of one chip.

For the maximum incoming Doppler frequency considered in this article (10 kilohertz), the slip of one chip occurs after 154 milliseconds for a GNSS signal at L1 and after only 12 milliseconds for GNSS L5 signals. For GPS L5, for example, that means the previously computed dwell time of 217 milliseconds would not be realistic as it implies a slip of 18 chips. So, the code Doppler clearly needs to be dealt with in a GPS L5 or Galileo E5a/E5b receiver, and potentially in a GPS L1 C/A, GPS L1C, or Galileo E1 OS receiver.

To complete this part of our investigation, Figure 10 presents the losses on the maximum amplitude of the squared autocorrelation function. The maximum losses are for L5/E5 signals, because these experience a slip of more than one chip (Figure 6). The minimum loss is for GPS L1 C/A (1.9 decibel for a code Doppler of 10 kilohertz), which is better than GPS L1C (2.5 decibels) and Galileo E1 OS (4.5 decibels) due to its BPSK modulation, even if the dwell time is longer (126 milliseconds instead of 50 or 80 milliseconds).

Resulting probability of detection. Let us now consider the resulting probability of detection taken in = 0 (Figure 11). Clearly, for GNSS L5 signals, the probability of detection decreases because the shift between the incoming and the local signals is too large.

Performance of the Acquisition-to-Tracking Transition
Once acquisition has been successful, the frequency estimate is on the order of a few tens or hundreds of hertz, depending upon the acquisition bin size. However, at the initiation of the tracking process, a refinement on the Doppler frequency is required in order to ensure locking the phase lock loop (PLL).

Frequency tracking. One solution is to use a frequency lock loop (FLL), which refines the estimation of the Doppler frequency. This is a critical stage in GNSS signal processing because, if this transition is not well calibrated, even a successful acquisition can lead to unsuccessful tracking, especially at low received C/N₀.

The authors undertook a performance study for various FLL schemes, which was described in the article by M. Foucras et alia (2014d) listed in Additional Resources. Based on the proposed test case, the probability of achieving FLL lock was analyzed assuming a C/N₀ of 27 dB-Hz. The four FLL discriminators examined in the study are the cross-product (CP), the decision directed cross product (DDCP), the differential arctangent (Atan), and the four-quadrant arctangent (Atan2). We should mention that during this initial phase of GNSS signal tracking being studied, bit synchronization has not yet been achieved.

Table 7 summarizes the expressions, linear regions, and characteristics of the four candidate FLL discriminators. As only two discriminators are bit sign–transition insensitive, this feature plays a key role in the choice of the discriminator for the best FLL scheme.

Probability of Successful Transition. The key figure of merit for the acquisition-to-tracking process is the probability of successful transition (or convergence) of the FLL, regardless of the initial frequency error after acquisition (within the correct acquisition bin, thus with a Doppler error within

Equation (6)

hertz in the proposed case) as a function of the GNSS signal and the FLL discriminator. The convergence is assessed by making sure that the loop is locked after 20 seconds of tracking. The probabilities are obtained based on 200 runs per configuration.

For the simulations, the article by M. Foucras et alia (2014d) (Additional Resources) showed that it is better to choose an FLL loop bandwidth B_L that is relatively reduced even though this reduces the response time of the loop. B_L = 1 Hz is used in the following results.

Finally, for composite GNSS signals, two techniques were investigated: the first one consists of tracking only the pilot component and the second one consists of tracking both components by computing a FLL discriminator based on an average of the data and pilot discriminators (thus using the whole available signal power).

Two figures present the probabilities of successful transition for a signal with a C/N₀ equal to 27 dB-Hz. Figure 12 considers the pilot-only cases whereas Figure 13 considers a scheme using the total available power. As expected, successful convergence depends upon the initial frequency error (it is better to start close to the correct value).

In the legend of each figure, the mean probability of successful transition in the cell is provided. As can be observed, for GPS L1 C/A, whatever the Doppler initial frequency error, the FLL always converges using the CP or Atan2 discriminators, thus finely dealing with bit sign transitions.

• For GNSS composite signals, however, this is no longer the case:
• For the GPS L1C signal, due to the presence of 75 percent of the signal power contained in the pilot component, the difference between the two schemes (considering pilot or both components) is slight. The performance for both bit transition–insensitive discriminators is similar and around 0.95 in mean value.

For Galileo composite signals, it appears preferable to use both components. In this case, for the Galileo E1 OS signal, the average value is 0.94 for the DDCP discriminator and has a performance very similar to GPS L1C. For the Galileo E5a or Galileo E5b signals (and GPS L5, not shown here), the probabilities to get locked are very low (mean value 0.23), which constitutes a significant problem for the acquisition-to-tracking transition. This can be explained by the short integration time (one millisecond) associated to these signals which implies a high correlator output noise variance for a signal with a C/N₀ equal to only 27 dB-Hz.

Secondary code acquisition performance. The pilot component was initially introduced to avoid the data bit transition problem on the data component. Indeed, the pilot component is free of transition once the secondary code is demodulated. This leads to the use of longer coherent integration for a more robust tracking.

In an article by M. Foucras et alia (2013), the authors provided a detailed analysis on the probability of acquiring the secondary code for several GNSS composite signals. The main conclusion of this study was that the C/N₀ threshold to acquire the secondary code with a very high probability was much lower than 27 dB-Hz and should not be a problem.

Conclusions
Signal acquisition is a crucial processing step in GNSS receivers. A useful signal must be extracted from the incoming signal that is assimilated in the background RF noise, and its parameters should be estimated. Due to these conditions, the acquisition process at low received C/N₀ is a challenge.

We conducted a detailed analysis of all the sources of acquisition degradations, treating each point separately as described in this article, to understand its specific effect. Our emphasis was on the probability of detection, voluntarily putting aside the time-to-acquire factor, which is operationally of equivalent importance. The article also concentrated on a specific test case, which was to be able to acquire a GNSS signal with a received C/N₀ of 27 dB-Hz with a probability of detection of 95 percent.

The first point that we addressed was the degradation of signal acquisition performance caused by the estimated parameters’ uncertainty brought by the size of the acquisition bin. A typical bin size results in an average degradation of the probability of detection on the order of 5 to 20 percent in the test case that we considered.

We then showed that the problem of the bit-sign transition was not a big issue for the acquisition of GPS L1 C/A. This is because a data bit transition can occur only every 20 spreading code periods, and a good choice of the coherent integration time enables a receiver to limit the degradation of acquisition performance.

However, for the new GNSS signals considered in our research, a bit sign (data or secondary code) transition can occur at each spreading code period, and the adverse effect on the acquisition performance can become substantial. As a consequence, we highly recommend use of a transition-insensitive acquisition technique for these signals even if they are more computationally expensive.

We also showed that an uncompensated code Doppler particularly affects the acquisition performance for GNSS L5 signals due to their high frequency chipping rate. If not taken care of properly, this effect results in a correlation function shape becoming rounded and flattened, leading to a potentially poor estimation of the incoming code delay.

Our research also showed that the BOC-based signals are more influenced by code Doppler due to the shape of their correlation function. As a consequence, if code Doppler is not taken into account by the receiver, it becomes necessary to limit the acquisition dwell time even if this penalizes the acquisition performance at low C/N₀ (it does anyway).

Finally, we described the use of FLL for the carrier acquisition-to-tracking process, with the main conclusions being to use bit transition–insensitive discriminators for composite GNSS signals.

Additional Resources
[1] Bastide, F., and O. Julien, C. Macabiau, and B. Roturier, “Analysis of L5/E5 Acquisition, Tracking and Data Demodulation Thresholds,” in Proceedings of the 15th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 2002), Portland, Oregon, USA, 2002, pp. 2196 – 2207
[2] Betz, J. W., and M. A. Blanco, C. R. Cahn, P. A. Dafesh, C. J. Hegarty, K. W. Hudnut, V. Kasemsri, R. Keegan, K. Kovach, L. S. Lenahan, H. H. Ma, J. J. Rushanan, D. Sklar, T. A. Stansell, C. C. Wang, and S. K. Yi, “Description of the L1C Signal,” in Proceedings of the 19th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2006), Fort Worth, Texas, USA, 2006, pp. 2080 – 2091
[3] Curran, J. T., “Weak Signal Digital GNSS Tracking Algorithms,” Ph.D. thesis, National University of Ireland, Cork, 2010
[4] Foucras, M., (2012) O. Julien, C. Macabiau, and B. Ekambi, “A Novel Computationally Efficient Galileo E1 OS Acquisition Method for GNSS Software Receiver,” in Proceedings of the 25th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2012), Nashville, TN, USA, 2012, pp. 365 – 383
[5] Foucras, M., (2013) and O. Julien, C. Macabiau, and B. Ekambi, “Probability of Secondary Code Acquisition for Multi-Component GNSS Signals,” in Proceedings of the 6th European Workshop on GNSS Signals and Signal Processing (SIGNALS 2013), Neubiberg, Germany, 2013
[6] Foucras, M., (2014a) O. Julien, C. Macabiau, B. Ekambi, and F. Bacard, “Probability of Detection for GNSS Signals with Sign Transitions,” IEEE Transactions in Aerospace Electronic Systems, submitted July 2014
[7] Foucras, M., (2014b) O. Julien, C. Macabiau, B. Ekambi, and F. Bacard, “Optimal GNSS Acquisition Parameters when Considering Bit Transi-tions,” in Proceedings of IEEE/ION PLANS 2014, Monterey, CA, USA, 2014
[8] Foucras, M., (2014c) O. Julien, C. Macabiau, and B. Ekambi, “Detailed Analysis of the Impact of the Code Doppler on the Acquisition Perfor-mance of New GNSS Signals,” in Proceedings of the 2014 International Technical Meeting of The Institute of Navigation, San Diego, CA, USA, 2014, pp. 513 – 524
[9] Foucras, M., (2014d) and U. Ngayap, J. Y. Li, O. Julien, C. Macabiau, and B. Ekambi, “Performance Study of FLL Schemes for a Successful Acquisition-to-Tracking Transition,” in Proceedings of IEEE/ION PLANS 2014, Monterey, California, USA, 2014.
[10] Jiao, X., and J. Wang, and X. Li, “High Sensitivity GPS Acquisition Algorithm Based on Code Doppler Compensation,” in IEEE 11th International Conference on Signal Processing (ICSP), Beijing, China, 2012, pp. 241 – 245
[11] Kaplan, E. D., and C. Hegarty, Understanding GPS: Principles and Applications, 2nd edition, Artech House, 2005
[12] O’Driscoll, C., “Performance Analysis of the Parallel Acquisition of Weak GPS Signals,” Ph.D. thesis, National University of Ireland, 2007
[13] Parkinson, B. W., and J. J. Spilker, Global Positioning System: Theory and Applications, Progress in Astronautics and Aeronautics, Vol. I, 1996
[14] Psiaki, M. L., “Block Acquisition of Weak GPS Signals in a Software Receiver,” in Proceedings of the 14th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 2001), Salt Lake City, UT, USA, 2001, pp. 2838 – 2850
[15] RTCA, Inc., “Assessment of Radio Frequency Interference Relevant to the GNSS L1 Frequency Band RTCA/DO-235B.” 13-Mar-2008
[16] Van Diggelen, F. S. T., A-GPS: Assisted GPS, GNSS, and SBAS, GNSS Technology and Applications Series. Artech House, 2009

(̂τ, ̂f_d)

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Q: How do measurement errors propagate into GNSS position estimates?

A: Not surprisingly, GNSS positioning accuracy is largely dependent on the level of measurement errors induced by orbital inaccuracies, atmospheric effects, multipath, and noise. This article discusses how, specifically, these errors manifest as position errors.

Q: How do measurement errors propagate into GNSS position estimates?

A: Not surprisingly, GNSS positioning accuracy is largely dependent on the level of measurement errors induced by orbital inaccuracies, atmospheric effects, multipath, and noise. This article discusses how, specifically, these errors manifest as position errors.

Estimating a Position
For the purpose of our discussion here, we only consider least-squares estimation with no a priori knowledge of the receiver’s position or time. To this end, least-squares assumes the measurements, ⃗z, are related to the states (position and clock bias, in the case of GNSS), ⃗x, as follows

Equation (1) (see inset photo, above right, for all eqautions)

where h(•) is the measurement model — assumed, for convenience, to be non-linear — and (⃗v) is the vector of measurement errors. This equation is linearized to yield

Equation (2)

where ⃗x₀ is the current estimate of the state vector (point of expansion), δ⃗x is the error of ⃗x₀ relative to the (unknown) true states, H is the Jacobian matrix (also called the design matrix, observation matrix, or geometry matrix), and δ⃗z is the misclosure vector, which is the difference between the true measurements (⃗z) and the measurements estimated from the current states (i.e., h(⃗x₀)).

The well-known solution to equation (2) is as follows:

Equation (3)

where R is the covariance matrix of the measurement errors. The initial state estimates are then updated as follows

Equation (4)

Because the model is non-linear, we can use iteration to converge to the final solution.

Role of GNSS Errors
For the purpose of this article, the pseudorange measurement equation (equivalent to equation [1]) is written as

Equation (5)

where ⃗P is the vector of pseudoranges from all satellites in view, ⃗ρ is the vector of geometric distances between the receiver and the satellites, b is the receiver clock error (common across measurements), and ⃗v is the aggregate measurement error from all error sources. Although we aggregate all of measurement errors together, individual components (e.g., troposphere) could be separated and easily worked through the following development.

Let us now consider the specific case where the initial state estimate was perfect such that ⃗x₀ = ⃗x. Although this is an unrealistic scenario (if you knew the true position in advance, you do not need GNSS!), it serves as a useful illustration of how measurement errors affect the final solution. Furthermore, since the least-squares approach will yield the same position estimate for all reasonable initial state estimates (in this case, “reasonable” would include a position accurate to at least 1,000 kilometers), this scenario is not limiting.

For the assumed case, the true value of δ⃗x is zero. It follows that if the value estimated from equation (3) differs from zero, this actually represents the error in the estimated states. To obtain a more explicit equation, we first compute the misclosure vector as follows

Equation (6)

In other words, the misclosure vector contains the measurement errors only. Finally, substituting this result into equation (3) gives

Equation (7)

Equation (7) shows how measurement errors propagate into the final solution. Although the equation is relatively simple, there is no hard-and-fast rule for describing how this happens. Rather, we can only say that two key things determine the effect of measurement error on the final solution: the relative measurement accuracy reflected in R, and the measurement geometry as reflected in the Jacobian.

Before looking at these aspects in more detail, note that equation (7) shows the effect on all state estimates separately (i.e., as a vector). This is important because some applications may be more interested in certain parameters than in others. For example, aviation is more sensitive to vertical positioning errors than horizontal positioning errors. In contrast, timing applications are not concerned at all with the position states.

Measurement Accuracy
Intuitively, the more accurate a measurement is assumed to be, the more weight will be given to that measurement. As R is the covariance matrix of the measurement errors, this weighting of the measurements happens “automatically” within the least-squares estimation process.

Of course, because the user (or perhaps software programmer) is responsible for selecting the covariance model, careful decisions need to be made in this regard; otherwise results will be suboptimal.

Measurement Geometry
To further explain the idea of measurement geometry, a single row of the Jacobian matrix (corresponding to the i-th single measurement) can be written as

Equation (8)

where ⃗u_i is the unit vector pointing from the receiver to the i-th satellite. The distribution of all satellites relative to the user reflects the measurement geometry. This is often quantified using dilution of precision (DOP) values.

To illustrate the importance of measurement geometry, consider Figure 1 (inset photo, above right), which shows two measurement scenarios for a two-dimensional positioning problem. In both cases, the receiver (blue) is measuring ranges (not pseudoranges) from the transmitters (red). Each transmitter is assumed to have an error of one meter, and all measurements are given equal weight (i.e., same variance).

The distribution of transmitters appears to be relatively similar; only one transmitter is moved (mirrored across the y-axis). Nevertheless, this small difference in measurement geometry results in different position errors.

Similar examples can be developed for the three-dimensional case, but this is more complicated to draw and is omitted here.

Unfortunately, users cannot place satellites to optimize measurement geometry. The best that can be done is to use mission-planning utilities to collect data during parts of the day where geometry is best (in the area of the data collection). Of course, using receivers that track satellites from multiple GNSSs will inherently improve the geometry too.

Estimating Clock Errors
The examples in the previous section only considered the case of measured ranges, meaning the clock error state does not need to be estimated. However, estimating the clock error — which is common across all measurements — can significantly affect results.

In particular, although we name the state the “clock error,” the estimated value will include the true clock error along with anything that appears to be common across all satellites.

With this in mind, if we repeated the previous examples using pseudoranges (thus requiring the clock error to be estimated), the fact that all measurements were assumed to have a one-meter error means that the least-squares estimator could not separate the true clock bias from the common error. The result would be that the clock error estimate would be biased by one meter (in this case), but the position error would actually be zero!

Different Types of Errors
Although equation (7) completely defines the propagation of a specific set of errors (i.e., at a particular instant of time) from the measurement domain to the position (and time) domain, this equation is usually reserved for systematic errors that manifest as biases in the short- or long-term. Such errors would include biases resulting from unmodeled atmospheric effects, satellite orbital errors, and so forth.

Measurement blunders would also be considered systematic errors. In fact, equation (7) is used when assessing the reliability and integrity of a positioning system in the presence of blunders.

Random errors such as multipath and noise, however, are usually treated a bit differently. Specifically, these errors are usually well characterized by their standard deviation only (i.e., no bias), meaning their effect can be completely reflected in the measurement covariance matrix.

If this is the case, the effect of these errors on the solution is directly obtained from the covariance matrix of the estimated parameters, which is computed as

Equation (9)

This is a by-product of the law of propagation of variances. As before the result is affected by the measurement geometry and the measurement accuracy.

Summary
This article looked at how measurement errors propagate into positioning errors. The primary factors affecting this propagation are measurement geometry and the measurement accuracy. This explains the motivation for receivers that minimize measurement errors (especially multipath) and that track as many satellites as possible.

The post GNSS Position Estimates appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

In recent years, numerous, relatively inexpensive hardware platforms for conducting scientific research using the software defined radio (SDR) paradigm have become commercially available. The Manufacturers section near the end of this article lists examples of several of these. In turn, this has spurred universities and research groups around the world to adopt this technology for advanced GNSS signals-based research and development.

Popular research topics exploiting GNSS SDR receivers include “first look” GNSS signal capture and analysis, interference/spoofing detection and mitigation, GNSS signal authentication by means of nominally present satellite signal distortions (i.e. signal “fingerprinting”), signal quality and deformation monitoring, GNSS bi-static radar and synthetic aperture radar (SAR)-based imaging, multi-platform combined GNSS signal processing, advanced GNSS multipath mitigation, multi-element phased array processing, ultra-tight integration of GNSS with multiple sensors, vector tracking loops and other “holistic” and “open loop” signal tracking approaches, ionospheric research using multi-frequency GNSS observables, and general multi-constellation/multi-frequency GNSS receiver development, prototyping, testing and algorithm validation.

The general approach to carrying out such research involves one or more data collection campaigns followed by mulƒtiple cycles of algorithm development, sampled data processing, and analysis. Realtime processing capability is generally not required at this stage of development. However, to achieve maximum productivity researchers find it highly desirable to have flexibility in algorithm development by way of high-level programming languages and robust user-friendly development environments with extensive built-in math library support and data visualization capabilities. Arguably, within the satellite navigation community, MATLAB has become the de facto standard in this regard.

Many of the afore-mentioned research topics can involve sampled signal data collection at wide bandwidths, high dynamic range, and multiple coherently sampled streams. For example, consider a wideband GNSS data collection campaign for investigating phased array based interference mitigation techniques using a seven-element, controlled reception pattern antenna (CRPA). In this case, bandwidth, dynamic range, and multiple channels are all in play.

Assuming typical front-end hardware specifications for such an application of 60 megasamples per second, 14-bit samples (extended to two bytes for data transfer) and eight channels (one channel being a separate reference antenna), the data capture rate equals 960 Mbytes/second. Even with lesser requirements, it is not uncommon to return from a collection campaign with multiple hundreds of gigabytes (if not terabytes) of data. Thus, a solution is needed to process such large datasets in a reasonable amount of time.

Today’s general-purpose desktop and laptop computers provide tremendous numerical computation capability at low power and cost. This is made possible with multiple processor cores — each clocking at multiple gigahertz and supporting wide single-instruction-multiple-data (SIMD) instructions. In addition, today’s affordable consumer-grade solid-state drives feature sustained read speeds on the order of 500 megabytes/second. Hence, these machines are good candidates for crunching through large amounts of SDR data. For further discussion about computational workloads, see the sidebar (at the end of this article), “Cost/Benefit Justification for a GNSS SDR Toolbox.”

Unfortunately, the layers of software abstraction built into high-level development environments to facilitate user-friendly coding is one of the main reasons why, in general, these tools cannot take full advantage of the computation capabilities of the platforms they run on. Thankfully, all such tools support extensions to allow users to integrate their own custom libraries written in low-level code. In MATLAB, this extension framework is known as “MATLAB executable” (MEX).

The goal of the work reported in this article is to develop a truly universal GNSS SDR processing toolbox for education and research that could be distributed in the form of a plug-in for high-level algorithm development platforms — specifically MATLAB. The following high-level features were envisioned for the toolbox:

• supports the ability to perform a wide range of cutting-edge GNSS signals-based research topics as described previously
• supports most SDR data file formats and front-end frequency plans
• supports all current and emerging GNSS signal structures and other signals of opportunity
• has a user interface and configuration methodology that is easy to learn and apply to the various research topics described here
• provides as many open-source functional examples as possible, thus shortening the learning curve for both beginners as well as advanced users.

The work described in this article achieves, to a large extent, all of these objectives and, more importantly, builds the framework for the baseband signal-processing layer of a truly universal GNSS SDR architecture. The toolbox has been used successfully to process the following open GNSS signals using live data: GPS L1 C/A, GPS L2C, GPS L5, GLONASS FDMA signals on L1 and L2, Galileo E1 CBOC signals using BOC(1,1), BOC(6,1) and CBOC(6,1,1/11) processing; Galileo E5a and E5b, BeiDou B1, satellite-based augmentation systems (WAAS and EGNOS), and WAAS signals on L5.

The software is currently distributed as a MATLAB toolbox and can be downloaded free of charge for education and research use.

One important note: this toolbox is not a complete GNSS receiver in the sense that it does not output position, navigation, and time (PNT) solutions. However, the processed-signal outputs (available at a one-kilohertz rate) contain all the information needed for subsequent processing of PNT solutions.

Supporting Multiple GNSS SDR File Formats
Most SDR data collection systems store their IF-sampled or baseband-sampled data in binary format. For uninterrupted collections over prolonged intervals, data are sometimes written to multiple small files because such a strategy allows files to be managed more effectively than one file written to a large-capacity volume. For systems that collect SDR data continuously for the purpose of recording rare anomalous signal events, this multi-file collection strategy allows older files to be deleted to make space for new ones, thus extending the availability of past history to the size of the storage array in contrast to the capacity of a memory-based buffer.

In some systems, the GNSS samples may be interlaced with binary data from other sensors such as IMUs, laser scanners or cameras to achieve inherent time synchronization between these sensors. In this case, additional metadata information is needed to extract GNSS samples from the file and, optionally, decode the sensor data.

Currently no standard exists within the PNT community that allows GNSS SDRs to work seamlessly with files written by any SDR data-collection system. This means that the user is forced to set data decoding parameters in an ad hoc manner. When files from a different system are sourced, these parameters and the decoder must be changed manually — a process that is prone to human error.

Part of the effort described in this article aims to address this issue so that files from any data collection system can be seamlessly integrated into any GNSS SDR processing platform. The proposed solution is to pair a metadata file with each binary data file. The metadata file includes all the information needed to integrate the SDR file into the processor and decode its contents.

As the format for the metadata file, eXtensible Markup Language (XML) provides a desirable option. All operating systems and application development suites support XML, which is a low-overhead human-readable format, thus providing a straightforward process to integrate it into any data collection system. Figure 1 shows an example of an SDR metadata file written in XML. It contains all of the necessary information to decode the multi-stream samples correctly as well as other information pertaining to the data collection campaign.

The SDR toolbox uses this metadata mechanism to open and decode SDR data files from many data collection systems. When opening a specified SDR file, the reader automatically parses the XML file and imports the metadata into the MATLAB workspace as a structure. For multiple files, the user specifies the name of the first file along with the maximum number of one-millisecond blocks to be processed. This information is used to automatically find and splice the necessary files to fulfill the request.

Supporting a Wide Range of Research Applications
In broad terms, GNSS baseband signal processing can be divided into three stages. The following sections summarize the features required in each of these stages to support a wide range of research applications.

Pre-Correlation Processing. As is well known, correlation losses become negligible for sample quantizations beyond two bits. However, this does not hold true in the presence of interference. In this case, we can use additional dynamic range to perform interference reduction processing prior to correlation. Typical pre-correlation processing includes sample covariance computation (for interference detection and location) and digital filtering and excision techniques applied in the time and/or frequency domains.

The various types of pre-correlation processing that a researcher may want to apply to a GNSS processing application could be supported by including 1) an optimized sample statistics processor, 2) a sample masking processor for blanking interference-dominated samples from being correlated, 3) a configurable time-domain filter implementation (such as Direct-Form II), and 4) a fast Fourier transform (FFT) engine for implementing frequency-domain interference detection and excision techniques.

These processing blocks could be integrated into the sample streams using a software plug-in interface. Since implementations already exist in MATLAB, developing a fully featured pre-correlation processor was considered a lower priority compared to the correlation engine. However, Version 3 of the toolbox does include a sample statistics and noise processor as described in below.

Correlation Processing. Three fundamental techniques exist for sample correlation: time-domain correlation, parallel frequency correlation, and parallel code correlation. The latter two methods provide a large number of correlation outputs corresponding to Doppler frequency offsets or code phases, respectively.

The limited resolution of parallel correlation algorithms and the inability to steer the local replicas that produce them with adequate precision (particularly with respect to code phase) preclude their use in precision signal tracking applications. The parallel code correlation algorithm is most efficient when researchers need a large swath of code correlation space observability such as during signal acquisition. Other uses include correlation space monitoring (also known as delay-Doppler map monitoring) for applications such as spoofer detection.

In any case, a low update rate on the order of one to several seconds is typically sufficient for monitoring applications. As MATLAB already contains optimized FFT implementations to write parallel correlation algorithms, no attempt was made in this version of the toolbox to accelerate FFT-based parallel correlators.

Many of the GNSS SDR research applications described here require several more time-domain correlators than the typical two to five needed for traditional signal tracking. Supporting a given processing algorithm for all current and future GNSS signals can become cumbersome due to their various signal structures.

The ability to instantiate any number of correlators per channel (where each channel can be setup for any GNSS signal structure), and have all these correlators and channels managed with little-to-no user intervention is one of the most desired features of a universal GNSS SDR. This is because it allows the researcher to focus on higher-level algorithm development without having to be concerned with correlator implementation details. Bringing this idea to fruition was one of the major goals and contributions of this effort. The following section describes the architecture of these universal GNSS correlators.

Post-Correlation Processing. Following sample correlation, the data rate is reduced to an easily handled value of one kilohertz. Most of the specialized GNSS signal processing algorithm development occurs in this post-correlation domain. This is also where the strengths of high-level algorithm development tools such as MATLAB shine in terms of a researcher being able to modify scripts and visualize the effects quickly and easily.

An SDR toolbox must feature an interface to and from this domain that is both efficient and intuitive in terms of configuring and controlling the various types of channels and correlators as required by the researcher.

Functional Architecture
This article serves as an introduction to Version 3 of the GNSS SDR toolbox. This version’s functional architecture is significantly different to that of the previous version (v2) that was described in the paper by S. Gunawardena (2013) listed in Additional Resources.

Figure 2 shows the high-level functional block diagram of the GNSS SDR toolbox for MATLAB. Sampled data streams are read from source SDR data files, followed by buffering and decoding into one or more data streams. The streams are fed into two main signal-processing blocks: a stream statistics and noise processor, and a multi-channel ChipShape correlation engine.

To maintain a regular channel architecture that is not specific to any GNSS signal structure, the toolbox uses memory codes exclusively for all pseudorandom noise and masking sequences. These codes are fetched from files and saved in a cache that is accessible to both processing blocks. This code cache is fully configurable by the user such that unused codes can be swapped out for new ones at runtime.

The stream statistics and noise processor computes sample means, variances, and histograms for every one-millisecond block of samples. Sample statistics provide a valuable low-latency “situational awareness” indication of in-band interference. Researchers can use the raw one-millisecond outputs of this processor to prototype a range of interference detection/monitoring algorithms. The toolbox includes commands to disable these computations if not used.

In GNSS receivers, a channel control state machine is typically used to handle the transition from acquisition to steady-state tracking (and subsequent reacquisition to tracking following loss-of-lock events). A low-latency signal-to-noise ratio (SNR) estimate is used as one of the inputs to this controller. Hence, the SNR calculation requires an estimate of noise power, in general for each sample stream.

Some receivers employ a spare channel to compute this noise estimate by correlating with a PRN sequence that is known to be absent in the data. The toolbox implements these noise correlators within the stream processor block. To reduce computation load, noise correlators implement only the real component, and the numerically controlled oscillator (NCO) phase register sizes are also smaller than those used for tracking channels.

As with the statistics processes, each noise correlator can be turned off to improve runtimes. Because these noise correlators can be set to correlate with any of the configured memory codes (including for example, a dedicated random-noise code of any length), the likelihood of significant cross-correlation with in-band signals can be minimized.

Version 2 provided instantiation of any number of correlator points per channel, where each could be connected to one or more universal code generators with independently variable relative code phase delay. Even though this architecture facilitates a wide range of applications, this earlier version repeated the underlying sample-level multiply-accumulate operations when points were placed with less than one-chip separation from each other.

Version 3 eliminates these repeated operations by natively performing ChipShape correlation on an array of points. Ranges of points from this ChipShape output vector can be combined (at the user level) to form any desired points of the traditional triangular correlation function. For binary offset carrier (BOC) signals, the need for a subcarrier replica in the correlation process is also eliminated because the user can apply any subcarrier function as part of the ChipShape-to-triangular conversion step described previously.

Further, by applying chip masking patterns that are specific to the spreading code (combined with long coherent integration), transients of the underlying signal can be observed at high fidelity.

This technique has applications in advanced multipath mitigation, signal quality monitoring and authentication. The paper by S. Gunawardena et alia (2012) listed in Additional Resources provides an overview of ChipShape processing, the concept of chip masking, and its applications in signal quality monitoring.

Figure 3 shows the functional architecture of a Version 3 channel. As with previous versions, the user can instantiate any number of these channels in the toolbox. The main user-configurable channel parameters are shown in red.

The Stream Index parameter selects the input data stream to be processed by a channel. Carrier wipeoff is performed on this selected stream using the replica generated by the carrier NCO — controlled by phase-rate commands updated each millisecond. The carrier-wiped stream is then sent to independent banks of correlators that perform ChipShape correlation for a one-millisecond block of samples. The result is a ChipShape vector for each bank that is transferred to the user space (i.e., MATLAB workspace).

Each ChipShape bank is configured independently by means of three parameters: the number of correlation points per chip N_C, the whole number of chips spanning to the early side N_E (relative to code NCO integer phase), and the whole number of chips spanning to the late side, N_L. Hence, the size of the ChipShape vector is given by N_C·(N_E + N_L + 1), and the spacing between points is given by 1/N_C.

As shown in Figure 3, ChipShape processing essentially splits traditional correlation into partial accumulations, where the fractional state of the code NCO determines the array index applicable to the partial accumulation being processed. Splitting the correlation operation in this way maximizes opportunities for these accumulations to be combined in user space to form numerous correlation and/or code discriminator functions depending on the application. Another welcome benefit is that this method reduces the dynamic range required to prevent overflow of these accumulators by a factor of 1/N_C compared to a traditional correlator.

Not shown in Figure 3 are the three levels of enable/disable logic featured in the toolbox to improve runtimes: 1) enable/disable entire channels that were instantiated (also disables channel NCOs), 2) enable/disable banks that were instantiated within a channel, and 3) enable/disable individual points within a given bank.

If a ChipShape correlator is implemented as described thus far, the output vector would simply be the differential of a traditional triangular correlation function. Although useful, it does not provide full insight into chip transition edges and their precise zero crossings. The rising, falling, and stationary parts of a GNSS signal’s underlying code sequence can be recovered by correlating with a local replica that corresponds only to these events (e.g., to recover the rising-edge, keep all -1 to +1 chip transitions in the code sequence and set others to zero).

As shown in Figure 3, this functionality is implemented by multiplying the carrier-wiped sample stream with an optional masking sequence. (In actuality the mask bit is used to disable accumulation for that sample.) Each bank is configured independently to point to any code and/or mask sequence stored in the Code Cache shown in Figure 2.

Applying the ChipShape Correlator
The native ChipShape correlator architecture of Version 3 significantly expands possibilities for advanced GNSS signals-based research beyond what was possible with Version 2. Among these are the following examples.

Built-In Acquisition and Rapid-Reacquisition. A dedicated channel can be instantiated for acquisition and/or rapid reacquisition. This channel’s ChipShape vector would span a significantly larger range of integer chip offsets with coarse inter-point spacing (e.g. N_C=2 for BPSK, N_C=4 for BOC(1,1), and so on). A given PRN can be acquired by pointing to the corresponding memory code and progressively searching for sets of code phase offsets and Doppler frequencies over time.

For reacquisition, the channel’s carrier and code NCOs are set with best estimates of Doppler frequency and codephase, respectively. In this case, ChipShape points corresponding to unnecessary span may be disabled to reduce runtimes. An estimate from the noise processor can be used as the basis for setting up the acquisition detection threshold.

Spoofer Monitoring. A civilian GPS spoofing scenario described in the paper by T. E. Humphreys et alia (Additional Resources) attempts to pull a receiver tracking channel away from the genuine signal’s correlation peak by coercing it to lock on to a stronger peak produced by the spoofer. Even if Doppler offset and codephase are perfect matches to the genuine signal, the superposition of the two would cause significant distortion of the ChipShape function due to the spoofer’s RF transmitter transfer function (which itself is a function of its characteristic analog RF components including modulators, amplifiers, filters and antenna).

A monitor/detector could be implemented using the GNSS SDR toolbox based on a high-resolution ChipShape output computed by an additional bank in each channel. To reduce runtimes (which corresponds to reducing power in a practical application), this bank can be activated at periodic intervals or at the onset of in-band noise power fluctuations (as monitored by sample variance and/or noise correlators), which is a “cheaper” first indicator of possible in-band interference.

Chip Edge–Based Code Tracking for Advanced Multipath Mitigation. As evident from the ChipShape functions shown in the examples section, zero-crossing rising-edge and falling-edge transitions are the highest-frequency components attainable from any received GNSS signal through correlation processing. This is true regardless of signal structure. Hence, code tracking techniques that are primarily based on these transitions stand to produce the best pseudorange accuracy and multipath mitigation performance possible for any receiver of that bandwidth. Researchers can use the highly configurable ChipShape outputs produced by this toolbox as an enabler for researching novel edge-based code tracking techniques for precision GNSS applications.

GNSS Signal Authentication. Variations present in signal transmission payloads of satellites are known to cause subtle signal deformations that are detectable using appropriate processing techniques. Not surprisingly, ChipShape functions are the cornerstone of these techniques. For authentication applications, the deformation caused only by the satellite payload (as a function of nadir angle) must be isolated from nuisance components that include multipath, receiver antenna/front-end transfer function (including any variations due to temperature, vibration, and aging), and ionospheric effects.

Signal-Processing Applications
This section provides two GNSS signal-processing examples that illustrate the configuration and capabilities of the toolbox.

Tracking and Eye Diagram Extraction for BPSK(1) Signals: GPS L1 C/A. For this example, a Version 3 channel was configured with five banks as follows:

• Bank 1: N_C=120, N_E=N_L=1, Code: “GPS C/A PRN,” Mask: “None”
• Bank 2: N_C=120, N_E=N_L=1, Code: “GPS C/A PRN,” Mask: “GPS C/A PP PRN”
• Bank 3: N_C=120, N_E=N_L=1, Code: “GPS C/A PRN,” Mask: “GPS C/A PN PRN”
• Bank 4: N_C=120, N_E=N_L=1, Code: “GPS C/A PRN,” Mask: “GPS C/A NP PRN”
• Bank 5: N_C=120, N_E=N_L=1, Code: “GPS C/A PRN,” Mask: “GPS C/A NN PRN”

where PRN is the C/A code PRN number used and “PP,” “PN,” “NP,” and “NN” correspond to masking codes in which adjacent positive (P) and negative (N) chip events are isolated from the original C/A code. (The distribution includes a utility that generates these and other masking code files from a given PRN code file.)

The GPS L1 C/A signal was pre-acquired using the FFT-based “Quick Acquisition” utility included in the distribution. The latest distribution includes a fully open-source, single channel–tracking script that features a built-in tracking state controller. This state machine was configured to pull-in from acquisition, perform bit synchronization, and settle with the following steady-state tracking loop parameters: 20 milliseconds pre-detection integration time, 18-hertz, third-order phase locked loop (PLL) bandwidth, one-hertz carrier-aided first-order delay locked loop (DLL) bandwidth, and coherent early-minus-late code phase discriminator with early-late correlator spacing of 0.0167 chips.

The final state activates banks 2 thru 5. Then, using the navigation databit sign derived from Bank 1 (i.e., sign[Prompt-Q]) to keep rising, falling, positive, and negative components of the underlying signal together, the one-millisecond ChipShape outputs are coherently combined for approximately 100 seconds. Accompanying figures show the resulting normalized ChipShape outputs.

Figure 4 shows the GPS C/A code eye diagram from a GPS front-end module with approximately four megahertz bandwidth. The effect of narrow front-end bandwidth compared to the results depicted in the following two figures is clearly evident.

Figure 5 and Figure 6 show eye diagrams for GPS Block IIF-6 (SVN67 PRN06) at 78-degree elevation processed from data obtained with the TRIGR GNSS data collection system developed by the Ohio University Avionics Engineering Center. The final-stage IF filters for these two data streams included a transversal surface acoustic wave (SAW) filter with 24-megahertz/3-decibel bandwidth and a lumped element elliptic response filter comprised of a series of coaxial bandpass filters with 3-decibel bandwidth of 18 megahertz.

The bandwidth is sufficiently high in both eye diagrams to observe the 10 cycles of ripple that occurs within a C/A chip. As described in the article by S. Gunawardena et alia (2012b), these oscillations have been determined to be crosstalk from the P(Y) code modulation.

Careful observation of time intervals just prior to a chip transition in Figure 6 reveals a slight buildup of power. This effect, not observable in Figure 5, is primarily due to the finite impulse response-type characteristic of transversal SAW filters as will be reported in detail in a forthcoming presentation by S. Gunawardena et alia (2014) at the ION GNSS+ in September.

Tracking and E1C/E1B Subcarrier Extraction for CBOC(6,1,1/11) Signals: Galileo E1. For this example, a Version 3 channel was configured with four banks as follows:

• Bank 1: N_C=120, N_E=N_L=1, Code: “GAL E1C PRN,” Mask: “None”
• Bank 2: N_C=120, N_E=N_L=1, Code: “GAL E1B PRN,” Mask: “None”
• Bank 3: N_C=120, N_E=N_L=1, Code: “GAL E1C PRN,” Mask: “GAL E1C FF PRN”
• Bank 4: N_C=120, N_E=N_L=1, Code: “GAL E1C PRN,” Mask: “GAL E1B FF PRN”

where “FF” corresponds to masking sequences where adjacent chips with the same sign are isolated from the original spreading code.

Banks 1 and 2 are used for pilot signal tracking and data symbol extraction, respectively. The ChipShape outputs from these banks are correlated with the ideal CBOC subcarrier functions to produce traditional early, prompt, and late correlation points.

Banks 2, 3, and 4 are initially deactivated. After steady-state tracking is reached, the ChipShape outputs from Banks 3 and 4 are coherently integrated for approximately 100 seconds by performing symbol wipeoff using the known overlay symbols and the data symbols derived from the prompt correlator of Bank 2, respectively.

Similar to the GPS C/A code tracking example, the channel state machine was configured to obtain the same steady-state tracking parameters. However, instead of the bit synchronization state used for GPS C/A code, the Galileo E1 tracking demo uses the included overlay code synchronizer.

Figure 7 shows the one-millisecond prompt correlator outputs (pilot and data) from the acquisition pull-in state to just after activation of banks 2–4.

Figure 8 and Figure 9 show the Galileo FM3 E1 CBOC(6,1,1/11) pilot and data component subcarriers as observed from front-end bandwidths of 18 and 24 megahertz, respectively. As to be expected, the multi-level subcarrier functions experience more distortion with the 18-megahertz front-end compared to 24 megahertz. Also notice that for traditional early-minus-late discriminator-based code tracking, zero crossings do not occur at zero codephase due to band-limiting.

Conclusion
This article introduced the GNSS SDR Toolbox for MATLAB (Version 3). This software performs GNSS SDR baseband signal processing using an optimized multi-threaded approach. The main motivation behind the development of this tool was to accelerate offline processing times for large GNSS SDR datasets. The toolbox improves runtimes by at least a factor of 30 compared to equivalent MATLAB-only scripts.

The main feature of Version 3 is a multi-channel universal GNSS ChipShape correlation engine that can be used as the foundation for advanced GNSS receiver development, algorithm design, and prototyping. It can also be used as an educational tool for demonstrating advanced GNSS signal processing techniques.

The Version 3 distribution contains numerous open-source scripts that demonstrate the setup and use of all major features. The toolbox is available free of charge for educational and non-commercial research use. The software and additional resources are available through the author’s blog: <ChameleonChips.com>. Minimum software requirements needed to run the toolbox include Microsoft Windows (32 or 64-bit) and MATLAB version 2007B or above.

Acknowledgment
This article was adapted in part from a presentation given by the author at the ION GNSS+ 2013 conference on September 19, 2013. The views expressed in this article are solely those of the author and not those of any other person, institution, organization, or entity.

Additional Resources
[1] Ettus Research, Universal Software Radio Peripheral (USRP) (accessed July 2014)
[2] Galileo Open Service Signal in Space Interface Control Document (OS SIS ICD), issue 1.1 (accessed August 2013)
[3] Gunawardena, S. (2007), “Development of a Transform-Domain Instrumentation Global Positioning System Receiver for Signal Quality and Anomalous Event Monitoring.” Electronic Dissertation, Ohio University, 2007 (accessed August 2013)
[4] Gunawardena, S. (2013), “A Universal GNSS Software Receiver MATLAB Toolbox for Education and Research,” Proceedings of the 26th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2013), pp. 1560-1576, Nashville, Tennessee, USA, September 2013
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[7] Gunawardena, S. (2012b), and F. van Graas, “Analysis of GPS Pseudorange Natural Biases using a Software Receiver,” Proceedings of the 25th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2012), Nashville, Tennessee, USA, September 2012
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[9] Humphreys, T. E., and B. M. Ledvina, M. L. Psiaki, B. W. O’Hanlon, and P. M. Kintner, Jr., “Assessing the Spoofing Threat: Development of a Portable GPS Civilian Spoofer,” Proceedings of the 21st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2008), Savannah, Georgia, USA, September 2008, pp. 2314-2325
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SIDEBAR: Cost/Benefit Justification for a GNSS SDR Toolbox

For GNSS SDR, the most numerically intensive computations involve correlation of hundreds of millions of signed integer samples for each second of processing. However, these samples are typically less than one byte. Through some straightforward pre-processing steps to reduce dynamic range, the result of correlation over a one-millisecond interval can usually be made to fit within 16-bit signed integers with negligible loss of performance.

Hence, these structurally regular fixed-point computations can be parallelized by factors of 8 or 16 using 128-bit and 256-bit wide Streaming SIMD Extensions (SSE) or Advanced Vector Extensions (AVX), respectively. (AVX has been supported in all x86 processors shipping since 2011.) Further parallelization over the available number of logical processors (up to 8 in most consumer PCs) can yield up to 128x theoretical performance improvement compared to un-optimized code.

Such optimizations require the correlation algorithm to be partitioned so that subsets of the computations can be performed independently in each processor. This type of “fine-grained” architecting of an algorithm to exploit the feature set of a particular generation of processors to the maximum extent possible is best done by human programmers as opposed to optimizing compilers.

Because sample correlation is such a critical component of any GNSS SDR and the algorithm essentially does not change significantly with sampling rate or GNSS signal structure, the cost of low-level optimization can be justified by considering the subsequent time savings that can be gained. This is especially true if the correlation engine can be architected such that it supports a wide range of applications and use cases.

The post A Universal GNSS Software Receiver Toolbox appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

In the past 20 years GPS has simultaneously revolutionized both our modern infrastructure (by providing real-time navigation, mapping, and timing support) and our geodetic/surveying capabilities (by providing millimeter/centimeter-level positioning). At this point, most of the GNSS innovations we expect to see in the next decade will come from calculating positions more accurately and faster, while expanding from GPS to use of all available GNSS signals.

Twenty years ago, in an article in ESA Journal (see Additional Resources section near the end of this article) Manual Martin-Neira presented a new application for GNSS. Instead of processing the direct GNSS signals for positioning, timing, and atmospheric studies, Martin-Neira suggested employing reflected GNSS signals as the measurement. The first GNSS reflection experiments were focused on altimetry, ocean winds, and soil moisture; later researchers evaluated GNSS reflectometry for sensing snow/ice and measuring vegetation growth.

Each of these reflection studies used GNSS instruments specially designed to measure reflected signals. In contrast, geodesists and surveyors use GNSS instruments that we know are designed to suppress reflected signals (more commonly referred to as multipath). While these reflections are known to affect the accuracy of positions derived from these instruments, there is still no standardized approach that models (and eliminates) the effect of reflections.

In principle, this would suggest that GNSS reflection research is irrelevant for the tens of thousands of geodetic-quality GNSS receivers currently in operation around the world. Certainly GNSS reflections were never considered as a potential source of soil moisture, snow depth, and vegetation data when the EarthScope Plate Boundary Observatory (PBO) was built in the western United States between 2005 and 2008.

Unexpectedly, we have shown that these environmental data can be retrieved from PBO data without any instrumentation beyond the existing GNSS data stream. We can do this because the multipath data turn the GNSS site into a quasi-interferometer. The distance between the antenna and the surface reflecting material derived from the interferometric effect will tell us whether the top of the surface has moved. This means we can use the data to measure snow depth by comparing it to data when there is no snow.

If the reflected signal travels through vegetation, the interferometer will show two effects: the primary reflection is caused by the top of the soil layer and secondarily, the amplitude of the reflected power will be smaller because it interacted with water in the vegetation. Changes in soil moisture cause the smallest changes to the interferometric effect. We can think of these as being caused by the signals being reflected by the surface soil layers having various wetness levels.

These new measurements of soil moisture, snow, and vegetation measurements (called the PBO H₂O network) are needed both for climate studies and satellite validation. Water managers use the data to predict, and hopefully mitigate, hazards such as floods and droughts. These new GNSS environmental data fill a niche between existing satellite sensors (that have very large footprints) and other in situ sensors (which tend to have very small footprints).

This article describes how we have created an operational GNSS environmental sensing network. We will first describe the network itself, followed by an overview of how reflections manifest themselves in GNSS observations, and ending with examples of environmental signals we have measured using this network in the western United States.

The Plate Boundary Observatory and Reflections
Consisting of about 1,100 stations, PBO was built by UNAVCO <http://www.unavco.org> under a contract with the U.S. National Science Foundation with the scientific goal of studying the motion of tectonic plates and the deformation of the North American continent. The locations of PBO sites (Figure 1) were chosen to address specific geophysical problems; thus, half of the GNSS sites are near fault zones in California. The east-west trending sites in Nevada and Utah are measuring motion in the Basin and Range province. Clusters of sites are also located near volcanoes (e.g. Yellowstone, Mammoth, Mount St. Helens, and the Aleutian arc).

After site locations were selected, the PBO project made special efforts to attach the GPS antenna to bedrock. Their reason for doing so was to ensure that the position (and velocity) information measured at each site would represent motion related to faults and volcanoes. For this reason, almost no PBO sites are located on buildings.

Figure 2 presents a schematic of a typical PBO site. A dual-frequency, choke-ring antenna is protected in an acrylic radome with a nearby equipment box housing the receiver and the telemetry hardware. The antenna’s “drill-braced monument” is anchored to a depth of nearly three meters.

The standard PBO site operates a dual-frequency carrier phase receiver collecting GNSS signal data with a 15-second sampling interval; most also support 1-second sampling. With the exception of some sites in Alaska, the data are telemetered to the central UNAVCO facility in Boulder, Colorado, after midnight UTC each day; files of carrier phase and pseudorange data (called RINEX files) are produced by UNAVCO and posted online for public access soon after. Geophysicists are able to download the RINEX files for reprocessing, or they can download the frequently updated position time series in a standard terrestrial reference frame.

Velocity products that are used to study faults, earthquakes, and volcanoes, are also produced for the geophysical community. These PBO positioning products are based on very detailed models of the GNSS spacecraft, propagation delays, and Earth motions.

Although geophysicists and geodesists are well aware of the negative effects of reflected signals, there is still no standard model to remove reflection/multipath from these position/velocity products. This is partly because each GNSS site has unique reflection characteristics. Furthermore, many efforts to model multipath rely on stacking carrier phase residuals from least squares analyses.

In principle these residuals could be used for environmental sensing; however, they can and will be influenced by mismodeled carrier phase data. Consequently, parameters in the least squares analysis could thus absorb or mask what was a real environmental change.

On the other hand, if one thinks about how best to measure multipath reflections rather than trying to model multipath corrections for carrier phase data, one might recast the problem to use signal power data. These are the analogous data to what is being used by the GNSS reflectometry community, which typically uses two receivers/antennas to separately measure the direct and reflected signal.

The GNSS units used by geodesists and surveyors produces a single data stream and measurements that represent the interference of the direct and reflected signal. In the latter case, the antenna is not tuned to measure the reflected signal as it is with traditional GNSS reflectometry.

So, a key question arises: Are the signal power data collected by geodetic/surveying GNSS units of sufficient quality to become inadvertent environmental reflectometers?

GNSS receivers generate carrier-to-noise density ratio data, which are stored in a RINEX file as signal-to-noise ratio (SNR) observables. Figure 3 shows representative SNR data set collected by a geodetic-quality GNSS receiver. The direct signal has a simple polynomial shape, with lower SNR magnitudes at the rising and setting sections of the satellite track. These lower values primarily result from the antenna gain pattern.

Superimposed on the direct signal are the reflected signals, which for horizontal planar reflectors manifest themselves as oscillations. Note particularly that little evidence of reflected signals appears above elevation angles of around 25 degrees. This is again due to the antenna gain pattern.

The transmitted GPS signal is right hand circularly polarized (RHCP). The reflection will have both RHCP and LHCP (left hand circularly polarized) components. As seen in Figure 4, the reflection coefficients are different for RHCP and LHCP, and depend on both the reflection surface and the satellite elevation angle.

The frequency of the reflected SNR signal is dominated by geometry, i.e., the extra path length traveled by the reflected signal, as seen in Figure 5. For a planar horizontal reflector, the frequency of the interference of the direct and reflected signal observed in SNR data is constant as a function of sine of the elevation angle. It is straightforward to extract this dominant frequency using a periodogram or estimate of the spectral density of the signal, a quantity that we call the effective reflector height.

If the effective reflector height changes, this means that the surface layer around the antenna changed. For example, an effective reflector height would change from 2.0 to 1.8 meters if it snowed 0.2 meters. To convert these effective reflector heights into an absolute measure of snow depth, we compare effective reflector heights estimated during the winter months with effective reflector heights determined when no snow is on the ground.

The amplitude of the reflection observed in the SNR data depends on the dielectric constant of the surface material — and, thus, very wet snow produces a different amplitude than very dry snow. Likewise, vegetation with high water content has much smaller SNR amplitudes than vegetation with very low water component. This is the principle used to define the vegetation statistic reported by PBO H₂O.

In order to define the snow depth, soil moisture, and vegetation water content measurements more rigorously we have developed forward models that contain information about the transmitted GPS signal, the gain pattern for the antenna used by PBO, and reflection coefficients for natural surfaces. These models have guided us in developing retrieval algorithms, which have been automated for PBO H₂O and published in the referred literature. As part of this effort we have also conducted validation experiments where we measured soil moisture, snow depth, and vegetation water content in situ. These experiments have been invaluable in allowing us to improve our algorithms.

Results from PBO H₂O
The PBO H₂O initiative grew out of experiments conducted near Boulder, Colorado between 2007-2009. After several years of developing models and retrieval algorithms, the PBO H₂O network began operating in October 2012 with a data portal providing online access to users. Figure 1 provides the location of the approximately 350 current sites along with about 200 new sites for which we plan to begin distributing data in the fall of 2014.

Data are downloaded from the central UNAVCO archive every evening, and new solutions for soil moisture, snow depth, and vegetation water content are posted each morning. To aid in quality control for our products, we also download other environmental datasets, such as hourly samples of modeled precipitation and temperature data from the North American Land Data Assimilation System and snow cover data from NASA’s satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) project. These are useful for identifying outliers in our vegetation and soil moisture products. Photographs, Google maps, digital elevation maps, and climatology information are also provided for each site.

The following sections describe a few examples from each environmental dataset.

Snow. Our first snow depth measurements were made in 2009 at a flat mesa site south of Boulder. Although the snow depth retrievals were successful, we needed to demonstrate that the technique would work in more challenging environments. Figure 6 shows the next snow site we tested. We chose a Niwot Ridge, Colorado, site because of its topographic variability (due to its location in a saddle at an elevation of about 3,500 meters), extreme cold, and very high winds. Power and Internet access was available from an existing scientific installation.

Five years later, the GPS snow depth time series from this site shows that the reflection method is robust, with very few data outages. Comparisons with in situ data (the pole in the photograph is measured roughly every two weeks) show that the method is also very accurate. Although the monument is three meters tall, as seen in the inset photograph in Figure 6, the antenna was almost buried in spring 2011. The latter was a banner snow year throughout the western United States, and a handful of PBO antennas were buried at snow peak.

Figure 7 shows snow levels measured at a PBO H₂O site near Island Park, Idaho. Unlike the station position time series generated for this site by geophysicists, which shows almost no variability, the snow changes at the site are quite dynamic. The first snowfall generally occurs at the same time each fall, but the peak snow amount is highly variable, as is the timing of snowmelt. The latter measurement is particularly important for predicting potential flooding. A video combining a series of photos of the station with corresponding weekly snow level plots may be viewed online here.

Vegetation. PBO H₂O vegetation measurements (NMRI, Normalized Microwave Reflection Index) are based on changes in the reflection amplitude, where values of zero represent the vegetation with the lowest water content. Figure 8 shows the vegetation data for a GNSS site in eastern Wyoming designated P042. This figure compares the NMRI vegetation water content estimates derived from GPS satellite data with the site’s normalized difference vegetation index (NDVI). The latter are optical measurements — typically generated at 16-day intervals — using MODIS sensors that measure greenness, with each pixel representing a 250-square-meter footprint.

Greenness correlates strongly with photosynthesis production, and thus NDVI is commonly used to study vegetation growth. To provide some context, Figure 8 also shows modeled precipitation data (which is not directly measured at PBO sites). A close correlation appears between the GPS NMRI data and NDVI (correlation coefficient of 0.86).

Particularly note the absence of greenness in 2012 and low GPS values during the 2012 drought. That year had low snowfall, a very hot spring, and very little rain. The P042 data record also shows a double growth peak in 2008. This phenomenon occurs when there are large gaps between rainfalls.

Similar sensitivity to vegetation growth is shown for California site P532 (Figure 9). Here both the GPS and NDVI records show strong sensitivity to the effects of drought, including 2014. Note that, although the rains in late February 2014 brought cumulative precipitation levels up to near the five-year average, vegetation water content as measured by NMRI has not recovered.

Droughts effects in 2007 and 2009 are also clearly visible. A further note: the GPS vegetation data have a shorter “season length” than the NDVI data, with NDVI having a consistently longer growth season than the GPS measurement of vegetation water content.

Because GNSS reflections are sensitive to vegetation water content and NDVI is correlated to chlorophyll production, the combination of these measurements provides better constraints to phenologists studying the influence of climatic variations on periodic plant life cycles.

Soil Moisture. Soil moisture is the most challenging water cycle parameter to measure with GNSS receivers and faces some limitations. First, the reflection technique cannot measure soil moisture if there is snow on top of the soil. For PBO H₂O sites in the Rocky Mountains, we must remove data affected by snow. Second, soil covered by vegetation with very high water content (such as alfalfa) requires a more complex model of the reflections than we currently use.

Even with these restrictions, we have found many PBO sites that generate accurate soil moisture records. Figure 10 shows such a record from a GNSS site near San Jose, California. Note that there is strong correlation between soil moisture changes and precipitation events, and then there is a “dry down.” This is consistent with the behavior of a shallow (0-5 centimeter) soil moisture instrument. The sensing depth of the GNSS method is determined by its transmission frequency (L-band).

Although measurements of soil moisture are needed at depth as well as the surface, these GNSS data are particularly useful for satellite validation (ESA’s SMOS mission and NASA’s upcoming SMAP launch) because these sensors also operate at L-band.

Expanding PBO H₂O to International GNSS Networks
Although the initial emphasis of our project was to use data from the PBO network, we must stress that no technical reason exists which prevents GNSS instruments operated by surveyors and transportation agencies from being used for environmental sensing. Both geophysicists and surveyors use dual-frequency carrier phase GNSS receivers — and, if properly configured, such receivers can generate SNR data that are suitable for reflectometry applications.

Configuration examples include requesting that the receiver track both legacy (L2 P-code) and new civilian (L2C) signals. The latter is preferred for reflection research because the code is public and the extracted signal power is higher. Second, some receivers produce SNR data rounded to the closest integer by default, which the station operator can easily change so that it generates a more precise SNR data stream.

To demonstrate that surveyor-operated GNSS sites can be used as snow sensors, we have recently partnered with two surveying organizations to expand PBO H2O. Figure 11 shows two examples from these efforts. In Colorado we have accessed data from the Mesa County Real-Time Virtual Reference Network; the Minnesota data are distributed by the State Department of Transportation. Because the Minnesota sites tend to be located near highways, we used Google Earth images to window the data we used to measure show depth. For the Colorado sites, we used both photographs and Google Images. In both cases accuracy of the snow depth estimates is equivalent to that recovered from the PBO sites.

Can we measure soil moisture, snow depth, and vegetation at all GNSS sites? Unfortunately, the short answer is “no.” Many GNSS sites have been installed on buildings and/or near parking lots, where reflections would be of little interest for the purposes described in this article. The locations of these sites also produce degraded positioning accuracy, but the degradation is often acceptable to the primary users of the data — geodesists, surveyors, and others.

The second limitation to using GNSS networks for environmental sensing has to do with data availability. While many organizations provide a RINEX file to national archives such as CORS, often these RINEX files do not include the SNR data. Furthermore, some archives degrade the RINEX files by eliminating observables and decimating the remaining data.

This makes it difficult — and in some cases impossible — to extract useful environmental data from these. Although these issues constrain the use of data from some existing GNSS sites, we hope that results from PBO H2O encourages future installations in locations that can measure positions and environmental changes simultaneously.

Final Remarks
Geodesists, geophysicists, and surveyors have all established large GNSS networks. Nearly all of them have open data policies and encourage broad usage of their data. The vast majority of GNSS data users focus on positioning, although the timing and atmospheric communities also value data from GNSS networks. Here we have shown how to further extend the value of ground GNSS networks by describing how to routinely measure soil moisture, snow depth, and vegetation growth. These data are valuable both to scientists and water managers and a cost-effective use of existing infrastructure.

Acknowledgments
This work has been a collaboration that includes many colleagues, students and post-docs: Felipe Nievinski, Ethan Gutmann, Andria Bilich, Karen Boniface, James McCreight, Cheney Shreve, Clara Chew, Sarah Evans, Evan Pugh, John Pratt, Penina Axelrad, and Praveen Vikram. The PBO H2O portal is supported by NSF EAR-1144221 and NASA NNX12AK21G. PBO is operated by UNAVCO for EarthScope, and supported by NSF (EAR-0350028 and EAR-0732947).

Additional Resources
GNSS-Reflectometry
[1] Cardellach, E., and F. Fabra, A. Rius, S. Pettinato, and S. D’Addio, “Characterization of dry-snow sub-structure using GNSS reflected signals,” Remote Sensing of Environment, Vol. 124, 122-134, 2012
[2] Egido A., and M. Caparrini, R. Ruffini, S. Paloscia, E. Santi, L. Guerriero, N. Pierdicca, and N. Floury, “Global Navigation Satellite Systems Reflectometry as a Remote Sensing Tool for Agriculture,” Remote Sensing, Vol. 4(8), 2356-2372, doi:10.3390/rs4082356, 2012.
[3] Garrison, J. L., and S.J. Katzberg, “The Application of Reflected GPS Signals to Ocean Remote Sensing,” Remote Sensing of Environment, Vol. 73(2), 175-187, doi:10.1016/s0034-4257(00)00092-4, 2000
[4] Garrison J. L., and A. Komjathy, V.U. Zavorotny, and S.J. Katzberg, “Wind speed measurement using forward scattered GPS signals, IEEE Transactions on Geoscience and Remote Sensing, Vol. 40(1): 50–65, 2002
[5] Gleason S, S. Hodgart S. Yiping, C. Gommenginger, S. Mackin, M. Adjrad, and M. Unwin Detection and Processing of bistatically reflected GPS signals from low Earth orbit for the purpose of ocean remote sensing, IEEE Transactions on Geoscience and Remote Sensing, Vol. 43(6),1229-1241. doi:10.1109/TGRS.2005.845643, 2005
[6] Katzberg S.J., O. Torres, M.S. Grant, and D. Masters, Utilizing calibrated GPS reflected signals to estimate soil reflectivity and dielectric constant: Results from SMEX02. Remote Sensing of Environment, Vol. 100(1), 17-28. doi:10.1016/j.rse.2005.09.015, 2005
[7] Martin-Neira M., “A Passive Reflectometry and Interferometry System (PARIS)-Application to Ocean Altimetry,” ESA Journal, Vol. 17(4), 331-355, 1993.
[8] Ruf, C., and A. Lyons, M. Unwin, J. Dickinson, R. Rose, D. Rose, and M. Vincent, “CYGNSS: Enabling the Future of Hurricane Prediction,” IEEE Geoscience and Remote Sensing Magazine, Vol. 1(2), 52-67, doi: 10.1109/MGRS.2013.2260911, 2013
[9] Semmling, A. M., and T. Schmidt, J. Wickert, S. Schon, F. Fabra, E. Cardellach, and A. Rius, “On the retrieval of the specular reflection in GNSS observations for ocean altimetry,” Radio Science, Vol 47, doi:10.1029/2012RS005007, 2012
[10] Yang, D., and Y. Zhou and Y. Wang, “Remote Sensing with Reflected Signals: GNSS-R Data processing Software and Test analysis,” Inside GNSS, September/October 2009, pp. 41–45
GNSS-Interferometric Reflectometry
[1] Chew, C. C., and E. E. Small, K. M. Larson, and V. Zavorotny, “Effects of Near-Surface Soil Moisture on GPS SNR Data: Development of a Retrieval Algorithm for Volumetric Soil Moisture,”” IEEE Transactions on Geoscience and Remote Sensing, Vol. 52(1), 537-543, doi:10.1109/TGRS.2013.2242332, 2014
[2] Larson, K. M., E. E. Small, E. Gutmann, A. Bilich, J. Braun, and V. Zavorotny, “Use of GPS receivers as a soil moisture network for water cycle studies,” Geophysical Research Letters, Vol. 35, L24405, doi:10.1029/2008GL036013, 2008
[3] Larson, K. M., E. Gutmann, V. Zavorotny, J. Braun, M. Williams, and F. Nievinski, “Can We Measure Snow Depth with GPS Receivers?,” Geophysical Research Letters, Vol. 36, L17502, doi:10.1029/2009GL039430, 2009
[4] Larson, K. M., and F.G. Nievinski, “GPS Snow Sensing: Results from the EarthScope Plate Boundary Observatory,” GPS Solutions, Vol 17(1), 41-52, doi 10.1007/s10291-012-0259-7, 2013
[5] Nievinski, F. G., and K.M. Larson, “Forward modeling of GPS multipath for near-surface reflectometry and positioning applications,” GPS Solutions, Vol. 18(2), 309-322, doi:10.1007/s10291-013-0331-y, 2014
[6] Ozeki, M., and K. Heki, “GPS snow depth meter with geometry-free linear combinations of carrier phases,” Journal of Geodesy, Vol. 86(3), 209–219, doi:10.1007/s00190-011-0511-x, 2012
[7] Small, E. E., and K. M. Larson and J. J. Braun, “Sensing Vegetation Growth with GPS Reflections,” Geophysical Research Letters, Vol. 37, L12401, doi:10.1029/2010GL042951, 2010
[8] Zavorotny, V., and K. M. Larson, J. J. Braun, E. E. Small, E. Gutmann, and A. Bilich, “A physical model for GPS multipath caused by ground reflections: toward bare soil moisture retrievals,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing” (JSTARS), Vol. 3(1), pp. 100-110, 10.1109/JSTARS.2009.2033608, 2010

The post Environmental Sensing appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

The small satellite “Technologie-Erprobungs-Träger 1” (TET-1) is the first spacecraft developed for the German Aerospace Center (DLR) On-Orbit-Verification (OOV) program, which provides flight opportunities dedicated to testing and qualification of new technologies in space. The satellite was lifted into a low-Earth orbit (LEO) on July 22, 2012, from the launch site in Baikonur, Kazakhstan.

The small satellite “Technologie-Erprobungs-Träger 1” (TET-1) is the first spacecraft developed for the German Aerospace Center (DLR) On-Orbit-Verification (OOV) program, which provides flight opportunities dedicated to testing and qualification of new technologies in space. The satellite was lifted into a low-Earth orbit (LEO) on July 22, 2012, from the launch site in Baikonur, Kazakhstan.

TET-1 carries various technology demonstration payloads, among them the Navigation and Occultation eXperiment (NOX). This payload consists of a geodetic-grade GPS receiver, which is connected via an antenna selector to two GPS L1/L2 patch antennas.

One of the antennas is mounted on the satellite’s zenith panel and receives signals primarily used for precise orbit determination (POD) experiments. The second antenna is pointed towards the anti-flight direction of the satellite for collecting measurements of low elevation satellites for ionospheric and tropospheric occultations. The antenna switch allows to select either the POD or the occultation antenna for signal reception.

This article describes the NOX payload on board the TET satellite in detail and analyzes the receiver’s tracking performance and the accuracy of its navigation solution. It also presents the initial tracking results of the occultation antenna, which demonstrate that GPS signals can be tracked through the ionosphere below the satellite’s local horizon — at a minimum, even down to the upper part of the atmosphere — with commercial-off-the-shelf (COTS) equipment.

Spacecraft Design & Operation
The spacecraft bus for TET is based to a large extent on the Bi-Spectral Infra-Red Detection (BIRD) satellite bus. The satellite has a height of 880 millimeters and a depth of 670 millimeters. In launch configuration, TET-1’s width is 580 millimeters, which increases to 1,540 millimeters with the solar panels deployed. Its total mass is approximately 120 kilograms, which includes 50 kilograms of payloads.

TET-1 is equipped with star sensors, sun sensors, gyroscopes, and magnetic field sensors for attitude determination. A set of four reaction wheels and magnetic coils provides three-axis stabilization. The satellite bus system is equipped with a single-frequency receiver with its own dedicated patch antennas. Note that this system, which provides positioning and timing for the satellite bus, is completely independent from the NOX payload. The satellite carries 11 payloads in total, including the NOX, which has been designed to demonstrate the suitability of COTS technology for space applications.

Figure 1 shows a schematic illustration depicting the front view of the TET-1 satellite without its multi-layer insulation. The figure shows the various components of NOX, which will be discussed in further detail in the following section, and illustrates the orientation of the body-fixed coordinate system of the satellite.

The TET-1 satellite is operated in different attitude modes depending on the payload operation or mission requirements. The two attitude modes relevant for the operation of the NOX system are the Earth-pointing mode (EPM) and the Sun-pointing mode (SPM). In the EPM, the satellite’s body-fixed “+x”-axis points into the direction of flight and the “+z”-axis is oriented towards the center of the Earth. The SPM is used to recharge the satellite batteries. For this purpose, the satellite’s solar panels, which are mounted on the “-z”-panel are pointed towards the Sun to maximize their power output.

TET-1 was launched into a sun-synchronous LEO orbit at a height of approximately 500 kilometers with an inclination of 97.5 degrees on July 22, 2012. After testing all satellite subsystems and payloads during the commissioning phase after launch, the OOV mission was conducted until October 2013. The satellite still continues operation and is now part of the Firebird mission, with the task of fire detection from orbit.

Overview of the NOX
The Navigation and Occultation eXperiment on TET-1 has been designed to demonstrate the suitability of commercial-off–the-shelf technology for space applications. Figure 2 provides a schematic of the NOX hardware layout. The experiment consists of a dual-frequency GPS receiver connected to an RF relay, which allows operators to select one of two L1/L2 passive patch antennas for signal tracking. A low noise amplifier (LNA) with a gain of 26 decibels at the L1 and L2 frequencies is used to ensure an adequate signal strength at the receiver input.

The receiver is standard off-the-shelf hardware. Standard receiver firmware, however, cannot be used for spaceborne applications, because height- and velocity-constraints prohibit the operation on board a satellite. The NOX receiver is therefore equipped with a special firmware with these NATO limits removed. In addition to these modifications, the Doppler search window has been increased 45 kilohertz to facilitate signal acquisition under high dynamics.

With its 48 channels the receiver can track L1 C/A code and L1/L2 P(Y) code signals of 16 satellites simultaneously. It provides pseudorange, carrier-phase, Doppler, and carrier-to-noise density ratio (C/N₀) observations for L1 C/A and L2 P(Y) signals and as well as pseudoranges and C/N₀ for L1 P(Y) at data rates of up to 10 Hz.

The receiver underwent extensive pre-flight testing to ensure its suitability for use in space. The papers by J. Leyssens et alia and M. Garcia-Fernandez, listed in the Additional Resources section near the end of this article, describe this testing in further detail. The receiver board is mounted together with an interface (IF) board on the “-y”-panel of the payload compartment of the satellite as shown in Figure 1. The interface board serves as the power and commanding interface between NOX and the TET satellite bus. It also contains a protection circuit against single-event latch-up effects and a switch line, which allows the operation of the RF relay for antenna selection.

The antennas of NOX are two identical patch antennas, which are mounted on different sides of the satellite bus structure without dedicated ground planes or choke rings. The antenna used for precise orbit determination is mounted on the “-z”-panel of the satellite. The POD antenna points towards zenith, when TET-1 is operated in Earth-pointing mode. This attitude is therefore preferred for the operation of the navigation system, because it maximizes the visibility of GPS satellites.

Restrictions of the battery capacity, however, require TET-1 satellite to be operated in Sun-pointing mode for recharging when it is not in the Earth’s shadow. As a result, the POD antenna’s boresight vector does not always point towards zenith, but deviates significantly from the local zenith vector during parts of the orbit when the batteries are charged. As a result, the antenna’s field of view will be obstructed by the Earth, which limits the number of satellites available for tracking. We will show, however, that the NOX payload can still provide robust navigation solution most of the time.

The second antenna for radio occultation is mounted on the “-x”-panel. It points towards the Earth’s horizon in anti-flight directions when the satellite is in Earth-pointing attitude mode. This antenna orientation facilitates the tracking of GPS signals through the Earth’s atmosphere for GPS radio occultation (RO) measurements, which we will discuss in more detail later.

As only one antenna can be used at a time, the LEO orbit and clock offset determination during occultation experiments is performed using measurements of the occultation antenna. This is a simplified concept compared to most modern RO missions, which use a dedicated navigation antenna in parallel to one or more occultation antennas.

We should mention that the RO experiment of NOX is not intended to routinely provide data for weather prediction or climate research. Its purpose is merely to demonstrate the capabilities and limitations of current COTS hardware, along the lines of a similar experiment conducted on board the MicroLab 1 in 1995 and described in the article by R. Ware et alia.

Receiver Performance
In the following subsections, we analyze the in-flight performance of the GPS receiver and the antenna system with respect to activation behavior and signal tracking characteristics. Where available, the on-orbit results are compared to pre-flight tests with a signal simulator.

Receiver Start-Up Behavior. Unlike the GPS receiver on the satellite bus, which is operated continuously during the entire mission, the NOX receiver is only activated during dedicated experiment time slots typically once a week. The experiments have a varying duration between 12 and 24 hours. The receiver is only supplied with power during these intervals and otherwise is completely switched off.

Upon activation at the beginning of an experiment’s time slot, the receiver starts to search for satellite signals. The receiver’s non-volatile memory still contains the last valid position solution, broadcast almanacs and navigation data obtained before the last deactivation. In the case of the NOX receiver, this information is used to compute a list of visible satellites, which are prioritized in the signal search. If this warm start acquisition fails because no satellite has been acquired after 45 seconds, a sequential search over the full GPS constellation is performed instead until a position fix could be successfully computed.

The receiver’s time to first fix (TTFF) is an important performance measure. The histogram in Figure 3 shows the TTFF statistics for 20 receiver activations using the POD antenna. These data indicate that the receiver has achieved a first position fix after less than two minutes in more than half of the cases. Note that this includes the time necessary for receiver boot and self-test.

The shortest and longest TTFF encountered in the 20 receiver activations are 85 seconds and 189 seconds, respectively. The mean and standard deviation of the TTFF is 2.03 ± 0.50 minutes, which show good agreement with values obtained from hardware-in-the-loop tests using a signal simulator. The TTFF for the simulated scenario yielded a mean and standard deviation of the TTFF of 2.56 ± 0.50 minutes, as described in the article by J. Leyssens et alia. The short TTFF clearly shows the benefits of a smart signal-search concept and optimal use of the 48 channels during acquisition.

It is interesting to note that the receiver has never acquired a position fix within the first 45 seconds of activation, meaning that the first fix has always been achieved after a signal search over the full constellation. Using almanac and navigation solution information from previous fixes during a warm start is obviously not helpful due to the long interruptions between consecutive activations of the NOX payload.

Tracking Performance. The number of simultaneously tracked satellites is a key parameter to determine the availability of a navigation solution. Figure 4 shows the statistics for the number of satellites tracked on L1 C/A-code and L2 P(Y)-code signals over the entire period of time when NOX was activated using the POD antenna. Statistics for L1 P(Y)-code are not displayed, because they are virtually identical with L2 P(Y) results.

The figure shows clearly that the receiver tracks between 8 and 12 satellites most of the time, which provides sufficient redundancy for a robust computation of a navigation solution. These results are consistent with results from signal-simulator tests. The peak for P(Y)-code is shifted slightly towards a lower number of simultaneously tracked satellites compared to C/A-code, which reflects a lower sensitivity and deferred acquisition of the semi-codeless tracking of P(Y)-code.

The receiver tracks four or more satellites for 99.99 percent of the time on C/A-code and 99.73 percent of the time on P(Y)-code. We can thus conclude that the receiver has a high availability of single- and dual-frequency navigation solutions.

In order to assess the measurement quality of the NOX receiver and antenna system, we analyzed the carrier-to-noise-density ratio (C/N₀). Figure 5 depicts a polar plot of the carrier-to-noise-density ratio (C/N₀) for C/A-code measurements based on 24 hours of data recorded on August 30, 2012, using the POD antenna. The coordinate axes in Figure 5 are aligned with the local frame of the antenna, which exhibits a different orientation than the satellite’s body-fixed coordinate axes.

The measured C/N₀ ranges from 30 dB-Hz near the horizon to approximately 50 dB-Hz at higher elevation angles. The C/N₀ pattern is not rotationally symmetric, but exhibits a clear azimuthal dependency. This effect is especially pronounced on the left side of the diagram and results most likely from the mounting position of the antenna close to the edge of the panel. Without a choke-ring or a dedicated antenna ground plane, the non-uniform satellite structure affects the gain pattern of the antenna.

Accuracy of Navigation Solution. The NOX receiver computes a navigation solution based on dual-frequency pseudorange observations and outputs results at intervals of 30 seconds. The receiver’s internal position filter has been turned off in the NOX experiment; thus, the reported positions correspond to independent epoch-by-epoch navigation solutions.

The errors of the receiver navigation solution are assessed by a comparison with a precise reference trajectory from a reduced-dynamic orbit determination based on carrier-phase measurements. In the next section we will provide more details on how the reference trajectory has been obtained.

For our analysis, we selected navigation solution results from a period of almost 24 hours on August 30, 2012. Figure 6 shows the errors in radial, tangential (in the direction of flight), and normal directions with respect to the orbital coordinate frame with the corresponding statistics listed in Table 1.

It becomes obvious that the errors of the radial component exhibit the largest scatter, which is an expected result, because the vertical component is always most affected by the largest dilution-of-precision in a single-point solution.

For the majority of the epochs, the radial errors fall between ± 5.0 meters, whereas the tangential and normal components are typically less than ± 2.5 meters. At one epoch, though, the radial error reaches 13.81 meters.

When interpreting these results, it is important to note that the reference solution refers to the satellite’s center of mass whereas the receiver’s navigation solution refers to the antenna phase center. The offset between these two points is on the order of 0.66 meter and is projected differently on the radial-, tangential-, and normal-coordinates, depending on the satellite attitude, and affects the error statistics.

Nevertheless, we can conclude that the receiver typically provides navigation solutions with meter-level accuracy. The satellite has alternately been operated in Earth-pointing mode during eclipse and otherwise in Sun-pointing mode. Despite the possible obstruction of the antennas field of view in Sun-pointing attitude mode, the receiver has provided continuous navigation solutions for the entire time due to its high number of tracking channels and fast acquisition of satellites.

POD Performance
In this section, we will present the results of precise orbit determination of NOX. Here we will briefly introduce the orbit determination process. As no reference solution is available for the satellite’s orbit, overlap comparisons serve as a metric to assess accuracy of the POD results. A phase-center pattern for the NOX POD antenna computed from carrier-phase residuals is also presented.

Reduced Dynamic Orbit Determination. Precise orbit solutions have been computed using measurements from the satellite’s POD antenna at an update rate of 30 seconds. As a first a priori trajectory, a single point solution based only on pseudorange measurements is computed. This coarse trajectory is smoothed using a least-squares filter to fit the satellite positions to a dynamic orbit model. This smoothed orbit is then used as an a priori orbit for a reduced dynamic orbit determination, where pseudorange and carrier-phase measurements are processed in a least-squares filter with a dynamical orbit model.

The estimation parameter vector comprises the satellite position and velocity state at the reference epoch, a scaling factor for the accelerations due to solar radiation pressure and atmospheric drag, as well as the ionosphere-free carrier-phase float ambiguities. Further, empirical accelerations in radial, along-track, and cross-track direction are estimated to compensate for deficiencies in the deterministic model. A more detailed description of the POD procedure and the orbit model can be found in the article by O. Montenbruck et alia cited in Additional Resources.

As a more precise reference solution or independent measurements from satellite laser ranging are not available, a direct assessment of the errors in the precise orbits is not possible. Therefore, orbit overlap comparisons are used here to yield at least an indication of the orbit quality. For this purpose, we computed 19 orbit solutions based on a data arc of five hours on August 30, 2012. The first hour of each orbit solution with the central hour of a previous orbit solutions, starting two hours earlier.

The mean values of the pseudorange residuals are consistently between 70 and 75 centimeters for all 19 POD runs. The carrier-phase residuals are two orders of magnitude smaller and vary between 7.5 millimeters and 8.5 millimeters. Figure 7 presents the results for the 3-D RMS overlap errors. The maxi-mum and minimum errors are 35 millimeters and 5 millimeters, respectively, with an average of 18 millimeters.

Experience shows that the inclusion of empirical accelerations in the estimation leads to a reduced stiffness of the solution, which allows the estimated trajectory to closely follow the observations. As a result, the overlap comparisons tend to be too optimistic and the true orbit errors can be expected to be larger. Based on experience of previous missions with dual-frequency GPS receivers, we would expect the achievable 3-D RMS accuracy to be on the order of decimeters or better.

Antenna Phase Pattern. Inspection of the carrier-phase residuals from the reduced dynamic orbit determination has revealed clear systematic effects with azimuth- and elevation-dependent variations. These systematic residuals are due to antenna phase pattern variations of the receiving antenna caused by the effect of the satellite’s structure on the antenna in the absence of a choke ring or ground plane.

An antenna phase-center variation pattern can be derived based on carrier-phase observations processed over a longer time interval. For this purpose, we grouped the residuals of the POD into azimuth and elevation bins depending on the direction of the received signal. The phase center variation correction is then computed as the average of the residual in this bin. The resulting phase pattern correction is then used again for a POD and refined with corrections based on the residuals of further iterations.

Figure 8 depicts the results for the POD antenna based on the data of all NOX activations between August and December, 2012. The maximum amplitude of phase center variations is ± 25 millimeters. The phase pattern exhibits an irregular shape with rapid changes between maximum and minimum variations. The standard deviation of the carrier-phase residuals in the POD can be reduced from 12 millimeters to 8 millimeters using this correction pattern.

Radio Occultations
During a radio occultation (RO), the signal of a GNSS satellite is tracked by a receiver on the opposite side of the Earth close to the horizon. Because the signal is received through the Earth’s atmosphere, it is affected by delays and bending depending on the refractivity of the atmospheric layer.

The refractivity can be approximated as a function of total-electron content in the ionosphere as well as temperature, pressure, and humidity in the troposphere. We can compute the bending angle and the corresponding ray height of the signal from carrier-phase measurements, which allows us to retrieve the refractivity index and solve for atmospheric and ionospheric parameters.

Due to the change in geometry between the two satellites, the signal is received through different layers of the atmosphere during an occultation event. If high rate carrier-phase measurements are available, bending angle profiles for different altitudes can be recorded. The derived atmospheric parameters serve as input data for weather prediction and climate research, which are the main motivations for radio occultations.

Figure 9 presents a schematic of a radio occultation. The GNSS and the LEO satellite travel with velocities of v^s and v_r, respectively. The direct straight-line connection between the LEO satellite and the GNSS satellite determines the straight-line tangent altitude (SLTA), which is the distance of this line to the surface of the Earth.

Due to the bending, the actual signal path does not follow this straight-line connection, but is curved around the Earth at an impact height a. This curvature is characterized by the bending angle α at the corresponding impact height. Due to the signal bending, the receiver can still track the GNSS satellite, even though it may be below the Earth’s horizon as seen from the LEO satellite. However, note that the bending depicted in the plot is highly exaggerated and does in reality not exceed one degree.

The fundamental relation to be solved for in RO processing is the dependency of the bending angle on the impact height. For this purpose, the carrier-phase measurements are corrected for geometrical range between LEO and GNSS satellite, receiver and GNSS satellite clock offsets, and relativistic effects, which only leaves the delays due to ionosphere and troposphere.

This residual is referred to as the excess phase delay. The LEO orbit and the clock offset corrections are obtained from a POD using the occultation antenna. The bending angle can then be retrieved from the change in excess phase, referred to as the excess Doppler, and the LEO and GNSS satellite velocity.

Figure 10 shows results for an occultation event of GPS satellite SVN 47 (PRN 22) on September 2, 2013, between approximately 17:42 and 17:48 UTC. The tangent point coordinates of the occultation are 28.18° S and 82.81° E, which correspond to a location in the Indian ocean, halfway between Madagascar and Western Australia. GPS measurements have been taken with a data rate of five hertz.

The top plot in Figure 10 shows the signal-to-noise density ratio (C/N₀) for L1-C/A code and L2-P(Y) code together with the straight-line tangent altitude. The plot starts at an SLTA of approximately 500 kilometers, where the signal of the GPS satellite is tracked through the upper ionosphere. The C/N₀ is practically constant at 46.6 dB-Hz and 33.3 dB-Hz for C/A and L2-P(Y), respectively, for almost the entire period of time.

Only during the last 30 seconds of tracking at an SLTA of 50 kilometers and less, the C/N₀ starts to drop and exhibit larger variations due to the signal attenuation in the lower atmosphere. Also note that the L2-P(Y) tracking is disrupted earlier than L1 C/A.

The bottom of Figure 10 plot shows the corresponding excess carrier-phase for L1 and L2 together with the slant ionospheric delay computed from dual-frequency carrier-phase measurements. The slant ionospheric delay for the L1 frequency I_L1 has been computed from

Equation (1) (see inset photo, above right)

. . . This geometry-free carrier-phase combination removes all frequency-independent terms such as geometry, clock offsets, and tropospheric delay. The delay has the positive sign convention of the pseudorange delay, even though it has been computed from carrier-phase measurements.

Note that the ambiguities and frequency-dependent signal delays do not cancel out in Equation 1. Therefore, the absolute value of the ionospheric delay cannot be recovered, but only the temporal variation.

As expected, the ionospheric delay is smallest at high altitudes, where the electron content is low. As the signal path proceeds into the lower ionosphere, the delay increases and reaches a peak at a straight-line altitude of approximately 300 kilometers. For lower tangent altitudes, the delay decreases again. The maximum amplitude of the ionospheric delay variation over the data arc is about five meters, or ~30 total electron content (TEC) units. Since the measurements were taken on the dark side of the Earth, the ionospheric electron content is low.

The excess phases for L1 and L2 depicted in the bottom plot of Figure 10 show a maximum amplitude of ~240 meters for a straight-line tangent altitude of -20 kilometers, where the signal is tracked through the lower part of the atmosphere. At higher altitudes, only small variations of the excess phases due to the ionospheric delays are present. As a result, the bending angle is small at high altitudes and increases rapidly as soon as the signal path enters the troposphere.

If the excess phases of Figure 10 are converted into excess Dopplers, the bending angle of the signal can be computed as a function of the impact height. Figure 11 depicts the corresponding results for the L1 and L2 frequency. The plot shows that the bending angle for the L1 and L2 frequency differ due to the frequency-dependent ionospheric delays. In order to remove this effect, an ionosphere-free (or neutral) bending angle (LC) has been computed as a linear combination of the L1 and L2 bending angles at the same impact height.

The minimum impact height for this occultation is approximately 7 kilometers. The bending angle at this height is approximately 0.6 degree. For higher altitudes, the bending angle decreases until it reaches a level of about 0.001 degree for impact heights larger than 50 kilometers. The bending angles exhibits only very low noise for impact heights lower than 40 kilometers.

At higher altitudes, the noise increases significantly. The reason for the different noise levels becomes clear from the plot of the excess phases in Figure 10. For high altitudes, the signal delays are small, and measurement noise and model imperfections dominate. Apparently, bending angles less than 0.001 degree cannot be observed, because they are below the noise floor in the current processing. When the signal crosses the troposphere, the delays grow quickly with decreasing impact height and become distinguishable from the noise.

Summary and Conclusions
This article as presented initial flight results of the Navigation and Occultation eXperiment on-board the small satellite TET-1. The experiment has demonstrated that commercial-off–the-shelf hardware can be used in space-borne applications, with only minor changes to the receiver’s firmware.

With the height and velocity constraints removed and an increased Doppler search window, the receiver has reliably acquired and tracked sufficient satellites for a continuous navigation solution, typically within less than three minutes. The 3-D RMS errors of the navigation solutions are on the order of a few meters only. No latch-ups or other receiver failures have been observed during the entire mission.

Plots of the C/N₀ variation and the phase pattern variations in the antenna diagram indicate an effect of the satellite’s structure on the antenna characteristics. If a ground plane or choke ring is used to mount the antenna, these effects can be expected to be less pronounced.

In the absence of a more precise reference solution or independent measurements, for example from satellite laser ranging, the precise orbit determination accuracy cannot be directly assessed. Orbit overlap comparisons have shown errors of a few centimeters, between the first hour and the central hour a two five-hour orbit arcs. The use of an empirically derived correction pattern for phase center variation could reduce the carrier-phase residuals of the POD, typically from 12 millimeters to 8 millimeters.

Radio occultation experiments have shown that dual-frequency carrier-phase signals can be tracked through the Earth’s troposphere with a data of five hertz. The data enables researchers to monitor the ionospheric delay and derive slant TEC variations of the upper atmosphere. A bending angle profile for L1 and L2 carrier-phase measurements has been derived in the troposphere down to an impact height of about seven kilometers.

The NOX experiment proves that a low-cost GPS system, which fulfills the requirements for precise orbit determination, can be realized with COTS hardware. This approach may be appealing for research groups seeking to gain inexpensive access to relevant data and even help to identify possibilities for cost reduction in future satellite missions.

Analysis of NOX RO observations has demonstrated that GPS signals could be tracked through the ionosphere and troposphere below the satellite’s horizon. We must note, however, that the performance of this setup not sufficient to produce RO data ready to be used for use in weather forecast or climate research. Several special modifications — such as open-loop tracking, an autonomous occultation prediction and channel allocation algorithm in the receiver, and a higher sampling rate as well as a high-sensitivity antenna system — would be needed to make this system competitive to modern RO payloads.

Acknowledgments
The authors would like to acknowledge the contributions of their former colleagues Cécile Renaudie and Miquel Garcia-Fernandez, who have worked on the design and manufacturing of the NOX experiment. The support of colleagues at Kayser-Threde GmbH during the design, implementation, and operation of NOX payload is greatly appreciated. The team at Septentrio is acknowledged for technical support and discussions.

The authors would also like to thank the TET operations team at the German Space Operations Center, especially Andreas Spörl, Andreas Pohl, and Jens Richter, for their help in planning and implementing the NOX experiments. DLR Space Administration is acknowledged for the free flight opportunity of the NOX experiment on-board TET-1.

This article is based primarily on a paper presented at the ION GNSS+ 2013 conference in Nashville, Tennessee, USA.

Additional Resources
[1] Eckert, S., and S. Ritzman, J. Eckler, and W. Bärwald,” “On-Orbit Verification with a Technology Test Carrier TET,” in Proceedings of 6th IAA Symposium on Small Satellites for Earth Observation, Berlin, Germany, April, 23–26, 2007
[2] Föckersperger, S. and G. Staton, and M. Turk, “Future Small Satellite EO Missions Based on TET,” in Proceedings of the Small Satellites Systems and Services Symposium 2012, Portorož, Slovenia, June 4–8, 2012
[3] Garcia-Fernandez, M., and O. Montenbruck, M. Markgraf, and J. Leyssens, “Affordable Dual-frequency GPS in Space,” in Proceedings of the 6th International ESA Conference on Guidance, Navigation and Control Systems, Loutraki, Greece, October 17–20, 2005
[4] Gleason, S., and D. Gebre-Egzabher, GNSS: Applications and Methods, Artech House, Norwood, Massachusetts, USA, 2004
[5] Hajj, G.A., and E. R. Kursinski, L. J. Romans, W. I. Bertiger, and S. S. Leroy, “A technical description of atmospheric sounding by GPS occultation,” Journal of Atmospheric and Solar-Terrestrial Physics, 64:451–469, 2002. doi: 1364-6826/02/
[6] Kursinski, E. R., and G. A. Hajj, J. T. Schofield, R. P. Linfield, and K. R. Hardy, “Observing Earth’s atmosphere with radio occultation measurements using the Global Positioning System,” Journal of Geophysical Research, 102(D19):23,429–23,465, 1997. doi: 0148-0227/97/97 JD-01569
[7] Lemke, N. M. K., and C. Kaiser, S. Föckersperger, G. Staton, and T. Stuffler, “TET-Based Small Satellite Family,” in Proceedings of the 63rd International Astronautical Congress, Naples, Italy, October 1–5, 2012
[8] Leyssens, J., and and M. Markgraf, “Evaluation of a Commercial-Off-The-Shelf Dual-Frequency GPS Receiver for Use on LEO Satellites,” in Proceedings of the ION GNSS, Long Beach, California, USA, September 13–16, 2005
[9] Markgraf, M., and C. Renaudie, and O. Montenbruck, “The NOX Payload-Flight Validation of a Low-Cost Dual-Frequency GPS Receiver for Micro- and Nanosatellite Applications,” in Proceedings of the IAA Symposium on Small Satellite Systems and Services (4S), Rhodes, Greece, May 26–30, 200.
[10] Markgraf, M., and P. Swatschina, “The Navigation and Occulation eXperiment (NOX) onboard TET-1,” presented at 2nd TET Customer Day, Kayser-Threde, Munich, Germany, July 5, 2010
[11] Melbourne, W.G., and E. S. Davis, C. B. Duncan, G. A. Hajj, K. R. Hardy, E. R. Kursinski, T. K. Meehan, L. E. Young, and T. P. Yunck, The Application of Spaceborne GPS to Atmospheric Limb Sounding and Global Change Monitoring, JPL Publication 94-18, 1994
[12] Montenbruck, O.,l and T. van Helleputte, R. Kroes, and E. Gill, “Reduced dynamic orbit determination using GPS code and carrier measurements,” Aerospace Science and Technology, 9(3):261–271, 2005. DOI 10.1016/j.ast.2005.01.003
[13] Ware, R., and M. Exner, D. Feng, M. Gorbunov, K. Hardy, B. Herman, Y. Kuo, T. Meehan, W. Melbourne, C. Rocken, W. Schreiner, S. Sokolovskiy, F. Solheim, X. Zou, R. Anthes, S. Businger, and K. Trenberth, “GPS Sounding of the Atmosphere from Low Earth Orbit: Preliminary Results,” Bulletin of the American Meteorological Society, 77, 19–40. DOI 10.1175/1520-0477(1996)077<0019:GSOTAF>2.0.CO;2
[14] Yoon, Z., and T. Terzibaschian, C. Raschke, and O. Maibaum, “Robust and Fault Tolerant AOCS of the TET Satellite,” in Proceedings of the 7th IAA Symposium on Small Satellites for Earth Observation, Berlin, Germany, May 4–7, 2009

The post GPS Receiver Performance On Board a LEO Satellite appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

As the Air Force contemplates changing its GPS III prime contractor in the face of program delays, sources say it is also weighing much broader program changes to help it weather years of continued lean budgets.

As the Air Force contemplates changing its GPS III prime contractor in the face of program delays, sources say it is also weighing much broader program changes to help it weather years of continued lean budgets.

Air Force Space Command (AFSPC) on June 4 released a request for sources able to produce up to 22 GPS III spacecraft — an effort to assess the industry’s capability to replace both Lockheed Martin, the current prime contractor, and Exelis, the subcontractor responsible for the navigation payload. That payload — and consequently the GPS III satellites themselves — has been significantly delayed, much to the ire of the Air Force.

The original $1.46-billion contract let in 2008 anticipated launch of the first GPS III by this year. And, indeed, for the first few years the Lockheed team was running ahead of schedule. However, Exelis — which in a previous corporate incarnation as ITT Space had a lead or contributing role in building all of the GPS navigation payloads — found itself unable to produce the GPS III version with the new civil GPS L1C signal and multiple legacy signals within the original timeline.

Numerous U.S. aerospace companies responded to the AFSPC solicitation, as well as at least one foreign company.

“Obviously we want a GPS III that does what its supposed to do, delivered on time,” Lt. Gen. Ellen Pawlikowski said at a press briefing during this year’s National Space Symposium, Defense News reported. Until recently, Pawlikowski headed the AFSPC’s Space and Missile Systems Center (SMC) at Los Angeles Air Force Base home of the GPS Directorate (GP).

SMC/GP intends to create a competitive environment through a two-phase process for producing up to 22 GPS III satellites after the first eight for which Lockheed has already been funded. Phase 1 entails award up to two Production Readiness Firm Fixed Price (FFP) contracts in Fiscal Year 2015 (FY15) that would conduct space vehicle (SV) and navigation payload critical design review (CDR) with demonstrations and qualification of the SV subsystem boxes.

Phase 2 would be a limited competition between Lockheed Martin and the selected contractor (or contractors) for up to 22 GPS III production SVs. An award for those satellites is anticipated in the FY17/18 timeframe to support a first SV available for launch no later than the first quarter of FY23.

Perspectives as to the AF’s intentions for the new solicitation vary widely among aerospace and defense experts. Some see it as merely a shot across Lockheed’s bow to indicate official dissatisfaction with the delays, some as a real opportunity for new entrants into the program, others as cover for a reworked Lockheed design for the GPS IIIs.

Opportunity for New SV Design?
Although the “sources sought” announcement indicates that military officials are looking for a production contractor and not one to tackle a development program, experts tell Inside GNSS that broader changes are being weighed.

Everything is in flux, said one well-informed source, who compared the pace and search for solutions to the sort seen during wartime. The GPS III solicitation had a deadline for responses of less than two weeks after publication of the announcement.

“There is a lot of pressure in terms of cost and in terms of scope,” the expert said. “I think it’s sort of a stay-tuned kind of deal.”

The expert, like the others who discussed the contract situation, spoke on condition anonymity in order to be able to discuss the program freely.

Among the options being assessed, another source said was launching at least some of the GPS satellites without the nuclear detection payload — an idea that has been floated before.

“There is a very strong possibility that a new GPS III without the (Nuclear Detonation Detection System [NDS]) on it could, in fact, be built,” said the expert.  What makes it possible, this expert said, was that enough satellites with the NDS payload would be on orbit after the 12th GPS III spacecraft is launched to fulfill the nuclear detection mission through 2040 — making it unnecessary to loft more.

“It’s an avenue that’s being explored,” said the expert. They can fly some cheaper GPS IIIs without NDS for a while, they explained. “If at that point they need to put it back they can.”

Pulling NDS from some of the GPS III satellites could allow dual launch of those spacecraft — at a significant cost savings. Though the Air Force announced earlier this year that it would not pursue dual launch, the GPS Program Office is keeping the capability in its plan for the spacecraft, explained the source, so that the option is available later if needed.

Search for Payload Supplier Alternatives
One of the conditions under the potential new contract is that the provider of the navigation payload be a firm other than Exelis. Although other U.S. firms might be able to provide that capability, sources suggested, the Air Force may be contemplating looking even farther afield.

“I know they’re looking at whether or not they can buy that package from Galileo and modify it enough for GPS,” said one source. “They’ve got a guy actually spending time and effort to do that analysis.”

The American arm of Europe’s Surrey Satellite Technology Ltd. (SSTL), manufacturer of the Galileo navigation payloads, is certainly willing to discuss the opportunity.

“We are very interested in the alternate source GPS III activities and looking to undertake studies and add value to whatever team we might be able,” said Douglas Gueller, the chief operating officer for SSTL’s U.S. subsidiary. His firm has manufacturing facilities in Denver, he said, and is now executing it first missions in the United States— which include payloads for the Air Force and JPL.  

One U.S company likely to have the necessary expertise is Ball Aerospace — which would also be happy to step in.

“Ball Aerospace seeks to assist the Air Force with affordability for GPS by leveraging its strong navigation payload capabilities,” said Roz Brown, Ball media relations manager, in response to a query on the announcement.

Boeing has confirmed its interest in competing for the potential post-deal and, as the prime contractor for the GPS Block IIF satellites now being launched, is the likely lead contender, sources agreed. Boeing acquired the space systems division

“Boeing continues to believe there are affordable low-risk alternate GPS solutions, and looks forward to supporting the Air Force in the Sources Sought process to best meet the future warfighter needs,” said Paula Shawa, spokesperson for the company’s Communications, Space & Intelligence Systems division in a statement.

Northrop Grumman has submitted a response to the Air Force, confirmed spokesman Lon Rains.  Space Systems/Loral, now known as SSL, wants to be counted in, although it has not yet stepped forward officially.

“SSL did not submit an RFI response for GPS 3 but that does not indicate a lack of interest in offering the USAF a low cost commercial bus solution,” said Chuck Cynamon, vice president for business development at SSL Federal in an emailed statement.

“While we do not build precision navigation and timing (PNT) payloads,” Cynamon added, “we have integrated PNT capabilities onto commercial satellites in the past.  If the USAF elects to move forward with an alternative acquisition strategy, we would be interested in opportunities to offer a commercially-based solution.”

Lockheed Martin and Exelis are hardly out of the picture, however, and have recently had some success with the navigation payload.

“Right now Lockheed Martin is focused on delivering GPS III satellites that meet all mission requirements at an affordable cost,” said Lockheed Martin spokesperson Chip Eschenfelder. “As with any complex development program, there are first article technical challenges; in this case, with GPS III, it has been with the new advanced navigation payload.”

Eschenfelder added, “Test data indicates we have resolved all known technical issues. Recently, the last major payload subsystem to be tested, the Mission Data Unit (MDU) — which is the heart of the payload — completed Acceptance Test and was added to the payload’s panel.  The completed navigation payload panel will now undergo final panel-level testing prior to delivery as a completed navigation payload this fall.”

Lockheed has closed its Newtown, Pennsylvania, facilities where the GPS III satellite was developed, with about 350 of those employees relocating to company sites in Sunnyvale, California, and near Denver, Colorado, where components will be manufactured and the satellites will be assembled.

““We plan to deliver the first GPS III satellite at the end of 2015,” he said.

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