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. DISPOSABLE DRONES
Washington, D.C.

1. DISPOSABLE DRONES
Washington, D.C.
√ UAVs have gone from sci-fi to ho-hum so fast that now the Navy is working on the Kleenex of drones — use it once, throw it away. Named CICADA for Covert Autonomous Disposable Aircraft, the paperlike gliders fit in your hand and are released in swarms from above. They are programmed with a set of GPS coordinates and that’s it — no motor, no camera, no propulsion system, and only 10 parts. The CICADAs drop quietly and get where they need to go remarkably well. The U.S. Naval Research Laboratory dropped some from 11 miles up and they landed within 15 feet of the target. They’re not flimsy, an NRL engineer told the Christian Science Monitor — the only thing that kills them is desert shrubbery.

2.  CYBER SHARK HACKS JET
Syracuse, New York
√ An April Government Accountability Office report said hackers could exploit in-flight entertainment systems to get access to avionics systems in today’s Internet-connected planes. Software firewalls, said the GAO, can be compromised like “any other software.” The FBI grabbed a cybersecurity researcher, Chris Roberts, off a United 737 flight to New York on April 15 after he had tweeted about the plane’s vulnerabilities and, they say, highjacked the navigation system. The Feds said he hacked Thales and Panasonic systems several times with his MacBook Pro and iPad. He was able to get physical access to the in-flight entertainment system through the Seat Electronic Box using an Ethernet cable, and one time overwrote code in the Thrust Management Computer and commanded the airplane to climb, according to the search warrant affidavit.

3. BEEN THERE DONE THAT
Westlake Village, Los Angeles, California
√ In-car navigation? Meh, buyers say. Most people aren’t too excited about route guidance systems when they think about buying a new car with all the technological bells and whistles. What they do like is safety — collision avoidance, blind-spot detection, and night vision systems. So says an April report by consumer market research firm J.D. Power in their 2015 Tech Choice Study. Why the lack of interest? Possibly because buyers find their car navigation function rather difficult to use, according to a previous study. And possibly because the ubiquitous and frequently updated smartphone does a better job.

4. SHOPPING SPREE
Silicon Valley, California
√ Apple’s buying frenzy for small, feisty mapping startups is all over the M & A news. In May, the company bought Coherent Navigation, Bay Area developers of high-precision GNSS technology for consumer applications. Founded by Stanford and Cornell engineers, the startup claims it achieves centimeter-level accuracy and jamming resistance by combining data from GPS and Iridium signals. What does Apple want with high-integrity GPS? Apple gave its standard reply to the New York Times: “We generally don’t discuss our purposes or plans.” The unpleasantness resulting from Apple’s bad iPhone map app in 2012 certainly helped inspire the company’s acquisition of Locationary, PlaceBase, Broadmap, Hotstop, Embark, WiFiSLAM, and other innovators in the LBS arena. And Apple’s new mobile operating system, iOS9, will arrive this fall with a better mapping application, CNBC reports.

The post GNSS Hotspots | May 2015 appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

One of the first feature articles I wrote as a newly minted GNSS magazine editor 26 years ago was about an advanced rail traffic management system based on GPS that Burlington Northern, with the help of Rockwell Collins, had designed and implemented.

Headed up by a couple of former NAVSTAR GPS Joint Program Office leaders — Don Henderson and Ed Butt — BN’s Advanced Railroad Electronics System (ARES) demonstrated its effectiveness on 250 miles of BN track in the Mesabi Iron Range from 1987 to 1992. ARES tracked and controlled seven locomotives and three maintenance vehicles, from a control center in Minneapolis, Minnesota.

I titled the article, which ran in the May/June 1990 issue of GPS World, “On Track with GPS.”

Everyone who came to Minnesota to watch ARES in action — including Federal Railway Administration (FRA) and National Transportation Safety Board (NTSB) officials, congressional staffers, shippers, Draper Lab analysts, and railroad executives — agreed that ARES was a fine example of positive train control (PTC). Using a GPS constellation that was only half-built and the much less robust computers and wireless communications of that era, PTC could still help avoid collisions, control train speed, and improve rail traffic efficiency for the nation’s railroads.

Fast-forward 26 years to a 50-mph–rated railroad curve outside of Philadelphia, Pennsylvania, where on May 12 an Amtrak passenger train left the tracks at 106 mph, killed 8 people and injured 200. Meanwhile, every day thousands of railcars — a 4,000 percent increase since 2008 — carrying highly explosive shale oil are being hauled through American cities and along the nation’s waterways and through other sensitive environments.

Positive Train Control — why is this still not a thing 26 years later? Mostly because of strong resistance from the rail industry and weak oversight by federal regulators.

NTSB has included PTC on its “Most Wanted List” every year from the inception of the list in 1990, but then the board doesn’t regulate U.S. railroads. The Rail Safety Improvement Act of 2008 (RSIA) mandated that PTC be implemented on so-called Class I rail tracks by the end of this year. That legislation came about after the collision of a California Metrolink commuter train and a Union Pacific freight train resulted in 25 deaths and 102 injuries.

In the wake of the latest Amtrak accident, FRA officials say they will issue an emergency order to begin implementing a train control system that notifies an engineer when a train exceeds the speed limit and automatically applies the brakes — that is, PTC. After a long series of oil car accidents, U.S. Secretary of Transportation Anthony Foxx ordered railroads to use stronger-walled railcars to transport oil and implement an automatic braking system to control speeds.

Will all this kerfuffle actually get PTC back on track? Well, as NTSB Chairman Christopher Hart told a U.S. House Committee on Transportation and Infrastructure subcommittee in April, not really.

“We know that several rail carriers have stated that they will not meet the 2015 deadline,” Hart said. “This is disappointing.”

At the same subcommittee hearing, Acting FRA Administrator Sarah Feinberg admitted, “Although the railroads subject to the mandate are working diligently towards implementation of PTC systems, FRA is concerned that the vast majority of these railroads will not be able to meet the deadline.”

Currently, PTC systems are in use on Amtrak lines only on the Northeast Corridor in the United States and on the Michigan line between Chicago, Illinois, and Detroit, Michigan.

Oh, I should mention that on May 14, 2012, the FRA issued a final rule that exempted about 10,000 miles — out of about 140,000 miles — of U.S. track from the RSIA’s PTC mandate.

As Mayor Tom Weisner of Aurora, Illinois, where more than 40 oil trains roll through town each week, told NPR news: the new rules are full of holes and do little to protect those who live near the rails.

“I don’t think our federal regulators did the job that they needed to do here,” he says. “I think they, uh . . . wimped out, as it were.”

The post Still Not a Thing, Part 2 appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Now that we have had GNSS-driven precision in the fields for nearly 20 years, with widespread and growing acceptance by farm vehicle manufacturers and farmers, what lies ahead for precision agriculture?

Now that we have had GNSS-driven precision in the fields for nearly 20 years, with widespread and growing acceptance by farm vehicle manufacturers and farmers, what lies ahead for precision agriculture?

The unobstructed views of the sky, which eased the task of ensuring robust signal availability for use with commodity crops such as corn and wheat, is narrowing as growers turn their attention to high-value orchard fruit and wine grapes. Moreover, the scarcity of skilled workers in some sectors is undercutting traditional reliance on manual labor for such tasks as pruning, chemical applications, and harvesting.

These forces have encouraged farmers to look toward increasing automation of equipment to ensure continued efficiencies on the farm. To help us sort out these issues, we turned to Francisco Rovira-Más, director of the Agricultural Robotics Laboratory (ARL) at Polytechnic University of Valencia. Dr. Rovira-Más obtained a Ph.D. in agricultural engineering from the University of Illinois at Urbana-Champaign in the United States.

Among the ARL’s activities is participation in the VineRobot project, an EU-funded effort to integrate machine vision, infrared, GNSS, and other technologies to optimize vineyard management, decision-making, and improve grape quality.

IGM: What are some of the more promising sensors and technologies being incorporated with GNSS to augment and enhance the precision guidance, navigation, and control (GNC) of automated farm equipment?

ROVIRA-MÁS: The major complement to global positioning is local perception. Agricultural environments are open and unpredictable; therefore automation and guidance can never be achieved safely unless vehicle surroundings are reliably sensed. In addition, the tight spacing between crop rows often results in tolerances of a few inches, where real-time fine adjustments from neighboring features are instrumental to avoid collisions.

Machine vision provides a rich source of information that can be analyzed with efficient processing techniques at high rates. However, working outdoors poses serious challenges for long-term stability due to the continuous changing in the relative orientation between the sun and the farm vehicle and the common presence of shadows and reflections. Stereoscopic vision, on the other hand, is more robust to changes in ambient light because intensity variations affect the left- and right-hand images similarly, allowing their pixel-wise correlation as long as a minimum level of texture exists, which is usually granted in off-road environments. The fact that stereo perception provides a 3D representation of a vehicle’s vicinity is key to detect obstacles and estimate how far away they are.

The main disadvantage of 3D stereo has traditionally been its computational cost limiting real time capabilities, although current processors perform excellently with images of moderate resolution. An alternative to computer vision for finding guidance cues is represented by laser rangefinders known as lidar sensors. These provide a faster response and can typically detect obstacles at greater distances, but they usually scan in one plane and, as a result, are more prone to noise, especially in the dusty atmosphere of field terrains.

IGM: Automated precision guidance of agricultural equipment has most commonly been associated with large-scale production of commodity crops in open spaces. However, GNSS and integrated positioning technologies are also being used in specialty crops, orchards, and vineyards. Could you comment on some of these applications?

ROVIRA-MÁS: The added value associated with specialty crops makes their growers prone to adapt new technologies such as precision farming, robotics, and information technologies. The high competitiveness of global markets and the lack of young farmers in industrialized countries practically leave the incorporation of automated or semi-automated technologies in the field as the only alternative.

The apple industry in Washington State has been demanding automated solutions for a long time, the citrus sector in Spain cannot cope with labor costs, and the winegrowers in France’s Burgundy region have trouble finding skilled workers for vine pruning in the winter. The requirement is basically the same: cost-efficient precise machines capable of delivering specialized work at a near-human pace.

The case of the wine industry is especially attractive for technology-based solutions, as wine can be considered a luxury rather than a basic product, and investment in high-performance equipment is easier to justify.

Premier wine requires the identification of grapes with homogeneous characteristics and here is where GNSS becomes irreplaceable, as mixing grapes of varying quality is the recipe for a mediocre wine. Global positioning allows the mapping of vineyards according to quality and harvest readiness, which in turn is the gateway to differential harvesting, the longstanding dream of many viticulturists.

Most medium-size orchard farmers, however, are not willing to pay a subscription fee for a higher quality differential signal; so, such commercial applications must offer safe solutions in light of this constraint.

Although differential corrections can remove important atmospheric errors, multipath and signal blockage often occur when a vehicle traverses an orchard or gets close to farm buildings. The main challenge is, therefore, finding the right balance between cost-efficiency and data robustness.

IGM: What issues, technical or other, still need resolution to realize the full potential of GNSS and related technologies in automated precision farming?

ROVIRA-MÁS: The most important issue by far is the long-term availability, reliability, and consistency of data. The rugged terrains and weather conditions of farm land require solutions comparable to those achieved by army vehicles but limited to much smaller budgets for equipment acquisition. All agricultural vehicles — intelligent or manned — have to be cost-efficient. The size and power of farm equipment ranges from gigantic harvesters of several tons and hundreds of horsepower to small scouting robots operating on electric batteries that are beginning to appear on the market.

IGM: What are the trends for operator involvement with integrated equipment? For instance, do they operate field vehicles remotely or on board?

ROVIRA-MÁS: So far, precision farming applications usually require operators to interact with a monitor on board the vehicle, but the current trend is to access and transfer data via mobile platforms such as cell phones and tablets. A crucial challenge for system integrators is the design of the user interface, as the complexity of operating these systems, regardless of their underlying intricacy, must be comparable to that of cell phone browsing or even less difficult. Intelligent off-road vehicles will generally be operated by farmers and field managers, who are not IT experts and have no time for tutorials.

IGM: Autonomous and semi-autonomous navigation raises issues of safety for agricultural workers as well as preventing damage to crops and the machines. What kinds of safeguards are being incorporated into GNC systems of automated field equipment?

ROVIRA-MÁS: All auto-steered agricultural vehicles require the presence of the operator inside the cabin. In automatic mode, when GPS signal reception is weak or if the driver stands up, removing weight from the seat, the vehicle stops. The only exception is California where a law allows automated machines to operate without a driver as long as there is a safety remote switch to control throttle, clutch, and brakes, and speed is below three km/h.

Many potential solutions never reached the commercial stage due to liability issues, especially with traditional equipment of considerable dimensions. New designs tend to reduce vehicle size to decrease the risk of accidents. For these cases, obstacle detection sensors — imaging or lidar — and a reliable GNSS fault-detector would suffice for performing specific farm tasks. Nevertheless, safety requirements remain a big barrier to widespread farm vehicle automation.

The post Farm Vehicle Automation appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

A: Most GNSS users are only interested in position, velocity, and/or time (PVT) information provided by a receiver. In fact, most mass-market GNSS receivers (e.g., those in cell phones or in your vehicle) only provide PVT information along with some supporting data (such as the number of satellites tracked, dilution of precision, course over ground, and so forth).

Q: Are there special considerations for dealing with raw GNSS data?

A: Most GNSS users are only interested in position, velocity, and/or time (PVT) information provided by a receiver. In fact, most mass-market GNSS receivers (e.g., those in cell phones or in your vehicle) only provide PVT information along with some supporting data (such as the number of satellites tracked, dilution of precision, course over ground, and so forth).

However, many — typically higher-cost — receivers also provide access to “raw” measurements including pseudoranges, Doppler shifts/frequencies (i.e., range rates) and, sometimes, carrier phase. Access to these raw measurements allows for powerful new data processing options, including development of a custom data processing engine (e.g., for a specific application), differential processing for higher accuracy, and/or tighter integration with inertial measurement units (IMUs) and other external sensors (see Demoz Gebre-Egziabher’s January/February 2007 “GNSS Solutions” article on different architectures for integrating GNSS and inertial data).

This article will touch on some of the unpublished and undocumented (or, at least, hard to find) differences that exist between receivers’ raw data that may trip up the “uninitiated.”

The Basics
As mentioned, raw measurements include pseudorange (P), Doppler (ϕ̇) and carrier phase (ϕ) data. Their simplified measurement equations are respectively given by the following:

P = ρ + b + ε_P
ϕ̇ = ρ̇ + ḃ + ε_ϕ̇
ϕ = ∫ϕ̇dt
= ρ + b + λN + ε_ϕ

where ρ is the geometric range between the user and the satellite, ρ̇ is the geometric range rate, b is the receiver clock bias scaled to units of distance by the speed of light, ḃ is the clock drift scaled to units of distance per second, ε are the measurement errors associated with the subscripted measurement, λ is carrier wavelength and N is the carrier phase ambiguity. Note that the carrier phase is the time-integral of the Doppler and is therefore sometimes called the accumulated Doppler range (ADR).

The main differences you observe between GNSS user equipment from various receiver manufacturers are: (1) the maximum allowable magnitude of the receiver clock bias and associated adjustments of the clock, and (2) the sign convention of the Doppler and carrier phase measurements.

Clock Effects
The receiver clock bias represents the difference between the receiver’s estimate of GNSS time and the true GNSS time (as maintained/transmitted by the satellites). This offset is theoretically unbounded, but all manufacturers try to limit it to some extent.

Millisecond Jumps. Most receivers will limit the clock bias to be less than some integer number of milliseconds, as determined from the receiver’s estimate of the clock error. Once the clock error exceeds a pre-set threshold, the receiver adjusts its time estimate by the requisite number of integer milliseconds needed to reset the error it to approximately zero—this is a so-called millisecond jump.

As I wrote in the March/April 2011 “GNSS Solutions” column, depending of your application, the magnitude of the clock error may or may not be important, and thus a review of that article is indirectly relevant in this context, too. However, the focus here is on the effect of the jump on your data processing and in this regard, three main issues arise: the magnitude of the millisecond jump, how it impacts the various measurements, and how it may affect your PVT solution. These are discussed in more detail below.

First, although this is the most common way of handling timing errors in raw GNSS data, receiver manufacturers will have different magnitudes of millisecond jumps ranging, in my experience, from 1 millisecond to as much as 100 milliseconds. Once scaled to units of distance by the speed of light, even a one-millisecond jump is relatively easy to identify in the data (because it creates about a 300-kilometer ranging error that, we will discuss later, may adversely affect the position solution). However, your software should to be able to handle a jump of any integer number of milliseconds.

Second, although the clock bias term appears in both the pseudorange and carrier phase equations, we generally only “see” the effect on the pseudorange measurements. To most easily explain this, recall that the carrier phase is the integral of the Doppler, which itself is proportional to the clock drift. Since the clock drift is unaffected by a millisecond jump, the carrier phase is unaffected unless the millisecond jump is somehow “added” to the carrier phase after the fact.

The main effect of only seeing millisecond jumps on the pseudorange will be if you apply carrier smoothing techniques. If your software does not account for differences between the pseudorange and carrier phase data, you may inadvertently introduce large biases into your smoothed pseudoranges. More importantly, if the smoothing filters (one per satellite) are not all working in steady state, the magnitude of the biases would differ between satellites, thus causing a jump in your PVT solution.

The final effect to be considered is how millisecond jumps affect the PVT estimation algorithm itself. To this end, millisecond jumps do not affect a least-squares estimator, precisely because such estimators have no time history. That said, the estimated clock drift would change by the magnitude of the millisecond jump.

In contrast, Kalman filters need to handle these jumps carefully or a large position jump will result. Innovation testing within the Kalman filter algorithm will easy identify these jumps. Unfortunately, innovation testing is usually performed on a per-satellite basis, so blindly apply such algorithms may result in you rejecting all of your measurements, usually for many consecutive epochs! You therefore need to handle the case where all (pseudorange) measurements exhibit the same jump between epochs and that the jump is close to an integer number of milliseconds.

Clock Steering. The alternative to millisecond jumps is when a receiver performs clock steering. In this case, the receiver adjusts the frequency of its internal oscillator to drive the clock drift to zero. In this case, the clock bias term remains very small (typically a few microseconds or less).

Clock steering obviates many of the problems associated with millisecond jumps. However, if you have the fortune of developing and testing data processing software with clock-steered data, beware that the software may not work as well with data from other receivers.

Sign Conventions
The other main difference between receivers is the sign convention of the Doppler and/or carrier phase measurements. Pseudorange measurements are excluded here because these are, by definition, directly proportional to the geometric range regardless of how they are generated with a GNSS receiver.

The Doppler shift is defined as the difference between the transmitted and received frequency, in this case, at the satellite and receiver, respectively. However, whereas one receiver manufacturer may define Doppler as the transmitted-minus-received frequency, another may adopt the opposite convention. This leads to a sign ambiguity. (Note that a sign ambiguity could also arise from mixing the radio frequency to a negative intermediate frequency in the receiver’s front-end — a process known as “high-side mixing”— but this would also require some changes to the design/ implementation of the tracking loops. Ultimately, however, the source of ambiguity is unimportant.)

Furthermore, as the carrier phase is the time-integral of the Doppler, the sign ambiguity usually extends to the carrier phase. In other words, if the pseudorange for a given satellite increases over time, the carrier phase may increase or decrease at the same rate (ignoring ionospheric divergence effects).

Notwithstanding the foregoing, the RINEX (the Receiver INdependent Exchange) data format defines that the carrier phase changes with the negative sign of the Doppler; consequently, further caution must be exercised.

So, what is the effect of these sign differences? As is often the case with GNSS, it depends.

First, the sign of the Doppler might affect a receiver’s computation of its velocity (see Salvatore Gaglione’s “GNSS Solutions” column in the March/April 2015 issue).

Second, the sign of the carrier phase with respect to the pseudorange is important for computing position when using pseudorange and carrier phase data together, and for carrier smoothing of the pseudorange. In both cases, the pseudorange and carrier phase data should increase or decrease at the same time (or, at least, the software should handle the case where they behave oppositely).

Third, the relative sign of the Doppler and carrier phase measurements needs to be handled if using Doppler measurements to identify cycle slips in the carrier phase data.

Finally, any other combination of pseudorange and carrier phase measurements will require that the sign conventions be handled properly. This would include, for example, generation of the pseudorange-minus-carrier (“code-minus-carrier”) combination often used to assess pseudorange noise and multipath effects.

Summary
This article has looked at some of the unpublished and undocumented differences that exist between raw measurements provided by different GNSS receivers. Fortunately, these differences are easy to handle but require modifications to the data processing software; otherwise the resulting PVT solution may contain large, unexpected, errors.

The post Are there special considerations for dealing with raw GNSS data? appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Global navigation satellite systems provide position, velocity, and time (PVT) solutions to users whose receivers calculate position based on one-way ranging from satellites. As is well-understood, a key step in the positioning process involves a determination of the difference between the time of signal transmission identified in the satellite’s broadcast navigation message and the time of its reception by user equipment.

Global navigation satellite systems provide position, velocity, and time (PVT) solutions to users whose receivers calculate position based on one-way ranging from satellites. As is well-understood, a key step in the positioning process involves a determination of the difference between the time of signal transmission identified in the satellite’s broadcast navigation message and the time of its reception by user equipment.

The accuracy of time of transmission depends on a satellite’s onboard clock stability, with the clock’s short-term stability affecting — among other applications — precise point positioning. Hence, it is very important to monitor the short-term stability of in-orbit satellite clocks and find out the onboard time error compared to the navigation system time.

GNSS satellites transmit navigation signals on L-band and/or S-band frequencies. Highly accurate receivers track carrier phase as well as code phase of transmitted signals and use the carrier phase observables to determine an in-orbit clock’s short-term stability.

These calculations estimate deterministic errors due to geometrical range and range rate by using precise satellite ephemeris data to estimate the short-term stability of the satellite clocks. However, the algorithms employed in these estimation techniques are limited during in-orbit testing of satellite carried out after the launch when precise ephemeris data may not be available for processing. So, it is very important to characterize the onboard clock for initial operations of navigation satellites once in orbit.

This article proposes a mathematical model to remove deterministic errors, without using satellite ephemeris data, to analyze the short-term stability of GNSS in-orbit clocks in the presence of adverse environmental and equipment effects. The proposed technique is useful for geostationary orbit (GEO) or geosynchronous orbit (GSO) navigation satellites where the carrier doppler rate remains constant for short periods of time due to the orbital characteristics.

Description of the Case Study & Algorithm
We chose observation times in which the doppler rate remains constant and free from higher order (greater than second order) doppler effects. In this way, to estimate short-term stability (≤100 seconds) we are able to remove the effect of carrier doppler without using a satellite ephemeris. Hence, from the carrier phase observations, the combined effects of doppler, doppler rate, onboard clock, and receiver clock deterministic errors are removed by a least-squares estimation method.

For analytical purposes, we used typical GEO/GSO navigation satellite signals-in-space (SIS) in a case study, comparing the measured results of the in-orbit satellite clock’s short-term stability against a time reference on the ground.

We begin by modeling carrier phase observables. In our analysis, we used the L5 band SIS to estimate onboard clock stability by measuring the carrier phase differences between the onboard-transmitted carrier phase and that received at the user equipment, which provides a satellite-to-receiver range measurement in terms of the number of carrier phase cycles.

Onboard transmitted carrier phase is generated from highly stable atomic frequency standard, and the receiver measures carrier phase using a reference source, typically a crystal oscillator. These measurements are affected by various parameters such as integer cycles ambiguity, ephemeris errors, satellite clock bias, receiver clock bias, ionosphere effects, troposphere effects, and receiver measurement noise.

The carrier phase observable model equation is

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

where:

L = measured carrier phase at L5 band in seconds
λ_L = wavelength at L5 carrier frequency
φ_L = measured carrier cycles
r = geometrical range between satellite and receiver
c = speed of light
N_L = integer cycle ambiguity
Δt_s = satellite clock bias (bias, drift, drift rate and relativistic error)
Δt_r = receiver clock bias
I = ionosphere delay in meters
T = troposphere delay in meters
φ_{multi L} = multipath delay in meters
ε_φL = carrier tracking error in meters.

A receiver cannot measure the absolute carrier phase difference between satellite and receiver. It measures carrier phase within 0 to 360 degrees of one cycle as a first measurement and then keeps track of change in carrier phase over a period of time. Hence, it has an ambiguity of integer cycles (as a residue of range) that can be estimated based on precise ephemeris parameters and code phase measurements.

Satellite clock errors are deterministic errors, which can be estimated, based on least-squares estimation method. Receiver clock errors are also deterministic in nature. To estimate onboard clock performance, the receiver clock stability should be at least one order of magnitude better than onboard clock stability so that the effects of receiver clock stability do not introduce uncertainties into the carrier phase measurements.

Ionosphere delay is inversely proportional to square of frequency and varies with local time and season. Ionosphere delay adds maximum range errors on carrier phase measurements over a day. Ionosphere delay can be removed using dual frequency measurements.

Troposphere delay affects signals at up to 50 kilometers of altitude and contains delays due to wet and dry components, which can be estimated based on available statistical models or local measurements. Multipath also affects carrier phase measurements, but its contribution will be much less compared to other error sources. The carrier-to-noise density (C/N₀) of the link will add to the carrier tracking error on carrier phase measurements.

Analysis Time Period for Estimation of Constant Doppler Rate
To estimate the short-term stability (≤100 seconds) of an in-orbit satellite clock ephemeris parameters are assumed to remain constant. Due to relative motion between satellite and receiver, doppler and doppler rate keep varying with time. So, without the knowledge of precise ephemeris parameters, doppler and doppler rate can not be estimated deterministically. In turn, these time-varying parameters, if not compensated for in carrier phase measurements, will affect the estimation of satellite clock stability.

To solve this problem, we used GEO/GSO satellites’ carrier phase data for periods during which the doppler rate is constant so that higher order effects would not be present and effects due to relative motion on carrier phase measurement could be estimated using a least-squares method. This approach is valid only for navigation satellites in GEO/GSO orbit and not applicable for satellites in middle Earth orbits (MEO).

We used longer-duration double-differenced carrier phase measurements to determine the constant doppler rate in order to estimate clock stability. This process was carried out for two successive days, and approximately 350 seconds (analysis time) of data from each day were used to estimate onboard clock short-term stability using the SIS.

Estimation of Deterministic Errors. As discussed in the section describing our modeling of carrier phase observables, deterministic errors arise from the satellite and receiver clocks as well as those related to relative motion. Our analysis used the stable oven-controlled crystal oscillator (OCXO) from a phase noise measuring instrument as reference source for the payload test receiver (PTR).

The OCXO’s short-term stability is one order of magnitude better than the onboard clock in the observed navigation satellite. So, the effect due to receiver clock stability is nullified in the carrier phase measurements, which were used to estimate and remove the combined deterministic effects of onboard clock errors, receiver clock errors and errors related to relative motion before estimation of clock stability. The combined deterministic error (C_e) model is given in equation (2):

C_e  =  Δt_r – Δt_s + d₀t + d₁ t²    (2)

where, Δt_s and Δt_r are the satellite clock bias and receiver clock bias, respectively. d₀ (sec/sec) and d₁ (sec/sec²) are normalized doppler and doppler rate, respectively.

Frequency Stability. The Allan deviation is the most important time domain measure of frequency stability. Similar to the standard deviation, it is a measure of the fractional frequency error and has the advantage of converging for most types of random clock noise.

In this analysis, we used an overlapped Allan deviation mathematical tool to estimate frequency stability. The result is usually expressed as the square root of the Allan variance, using two samples of fractional frequency errors to estimate the stability of frequency. The overlapped Allan variance in terms of phase data is given as follows:

Equation (3)

where, x_i is the i_th sample of the N phase data spaced by the measurement averaging time τ = mτ₀ (seconds). m is an averaging factor, and τ₀ is the basic measurement interval. The relationship between fractional frequency values and phase data is y_i = (x_i+1–x_i)/2.

Because the Allan variance is two-sample variance, any bias and drift components in the phase data will cancel out and thus not affect frequency stability estimation. However, the drift rate on phase data will affect the frequency stability estimation, as discussed in the articles by D. W. Allan and M. Y. Shin et alia listed in the Additional Resources section near the end of this article. Hence, before estimation of the frequency stability of an SIS signal, all possible deterministic errors must be removed from the carrier phase observables.

Test Setup
In the test setup, we used the PTR to obtain the carrier phase observables. The carrier phase measurement accuracy of receiver is better than one millimeter, which corresponds to a white noise less than 3×10-12 at one second. Short-term stability of the OCXO from the phase noise measuring instrument, which was chosen as reference source for measurements, is one order better than observed navigation satellite onboard clock and its stability is given in Table 1 (see inset photo, above right).

A choke ring antenna was used to receive the L5 band signals. Benefit of using choke ring antenna is stable phase center and higher multi-path rejection capabilities. Thus effects due to receiver measurement error, receiver clock stability and multi-path are negligible on carrier phase measurements. Figure 1 shows the test setup for estimating the clock stability.

Results
The carrier phase-observables model shows that carrier phase observables are affected by ionosphere delay as well as troposphere delay. Troposphere delay remains constant for short time durations. The change in ionosphere delay (for a short time period) as compared to the range rate is negligible.

As mentioned earlier, our analysis used at least 350 seconds of data for the estimation of the short-term stability of atomic clock, which will average out random errors. Hence, any change in ionosphere delay or troposphere delay up to 15 millimeters per second will not affect the estimation of short-term stability of the in-orbit clock. Integer cycles ambiguity is also a constant term, which will not affect estimation of clock stability. As discussed earlier, any multipath error and receiver measurement error are also ignored. Now, only deterministic errors due to onboard atomic clock, receiver clock, doppler, and doppler rate need to be estimated and removed from carrier phase observables as per the model proposed earlier.

In analyzing the results, navigation data is not used to estimate the short-term stability of clock. L5 band carrier phase data of a typical GEO/GSO navigation satellite has been collected through the PTR on day of year (DOY) 36 and 37 of 2014, which are shown in Figure 2 and Figure 3, respectively. The carrier phase was measured at a one-second rate. Double differencing of carrier phase data, which results in nothing but the drift rate, is also shown in Figures 4 and 5.

Our analysis selected data from the interval 5600-5950 seconds from the DOY-36 data set and the interval 4050- 4400 seconds from DOY-37 data set for estimation of clock stability, as no higher order errors are present in carrier phase observables during those intervals and the doppler rate is also constant. Figure 6 and Figure 7 shows the fractional frequency error after removing deterministic errors from carrier phase observables as per equation (2) using least-squares estimation method. Figure 8 shows the estimated stability of the onboard clock using one-way carrier phase measurement compared with performance of the time reference on the ground.

Conclusions
This article presented a technique to estimate short-term stability of an onboard satellite clock using one-way carrier phase measurements given a constant carrier doppler rate during the period of analysis. This technique estimates the combined deterministic errors, i.e., satellite and receiver clock errors, doppler and doppler rate. Results are shown for typical GSO navigation satellite SIS compared against ground results. Accuracy of this technique to estimate clock stability is 1×10-13, which is one order of magnitude better than ground performance. This technique will find applications during in-orbit testing of any navigation satellite constellation having GEO or GSO satellite.

Acknowledgments
The authors would like to express sincere gratitude to Shri D K Das, deputy director, ISRO SATCOM & Navigation Payload Area (SNPA), for his encouragement during this work. They would like to thank Shri Sumitesh Sarkar, group director, SAC Satcom & Navigation Payload System Engineering Integration & Check Group (SNSICG), for his valuable guidance and comments during this work. They would also like to thank Shri Alak Banik, program director for the Indian Regional Navigation Satellite Systems, for providing insight of atomic clock.

Additional Resources
[1] Aeroflex, COMSTRON PN9100A Frequency Synthesizer Module
[2] Allan, D. W., “Time and Frequency Time Domain Characterization, Estimation, and Predication of Precision Clocks and Oscillators”, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. UFFC-34, No. 6, 647–654, November, 1987
[3] Gonzalez, F., and P. Waller, “Short Term GNSS Clock Characterization Using One-Way Carrier Phase”, Frequency Control Symposium, Joint with the 21st European Frequency and Time Form, pp. 517–522, IEEE International, May 2007
[4] Hesselbarth, A., and L. Wanninger, “Shortterm Stability of GNSS Satellite Clocks and its Effects on Precise Point Positioning”, Proceedings of ION GNSS 2008, pp. 1855-1863, Savannah, Georgia, USA, 2008
[5] Shin, M. Y., and C. Park and S. J. Lee, “Atomic Clock Error Modelling for GNSS Software Platform,” Proceedings of the Position, Location and Navigation Symposium (PLANS) 2008, pp. 71–76, IEEE/ION, May 2008
[6] Waller, P. “In-Orbit Performance Assessment of GIOVE Clocks”, Proceedings of 40th Annual Precise Time and Time Interval (PTTI) Meeting, pp. 69-82, December 2008

The post Estimating the Short-Term Stability of In-Orbit GNSS Clocks appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Unmanned aerial vehicles (UAV) are finding increased application in both domestic and governmental applications. Small UAVs (maximum take off weight less than 20 kilograms) comprise the category of the smallest and lightest platforms that also fly at lower altitudes (under less than 150 meters).

Designs for this class of device have focused on creating UAVs that can operate in urban canyons or even inside buildings, fly along hallways, and carry listening and recording devices, transmitters, or miniature TV cameras.

Unmanned aerial vehicles (UAV) are finding increased application in both domestic and governmental applications. Small UAVs (maximum take off weight less than 20 kilograms) comprise the category of the smallest and lightest platforms that also fly at lower altitudes (under less than 150 meters).

Designs for this class of device have focused on creating UAVs that can operate in urban canyons or even inside buildings, fly along hallways, and carry listening and recording devices, transmitters, or miniature TV cameras.

Operational requirements for these kinds of UAVs typically encompass flying close to the ground and in relatively narrow spaces with a lot of obstacles. This introduces problems for a simplistic application of technologies used in larger UAVs. In particular, the rotary wing UAV platforms used in those scenarios provide vertical takeoff and landing and hovering capability, but they are intrinsically unstable systems requiring high-rate and accurate attitude and position data to be automatically controlled.

Automatic control of the degrees of freedom of such flying robots is the key factor to make them easily usable by a trained but not particularly skilled pilot; therefore, it is essential for such devices if intended for a wide commercial market.

Small, lightweight, power-efficient, and low-cost microelectromechanical system (MEMS) inertial sensors and microcontrollers available in the market today help reduce the instability of such platforms making them easier to fly. Current MEMS inertial measurement units (IMUs) come in many shapes, sizes, and costs — depending on the application and performance required — and are widely used as sensors for relative position estimation.

Although MEMS inertial sensors offer affordable, appropriately scaled units, they are not currently capable of meeting UAV requirements for accurate and precise navigation due to their inherent measurement noise. However, the accuracy of a MEMS-based inertial navigation system (INS) can be improved by integrating them with a GNSS receiver, simultaneously developing appropriate integration mechanisms.

This article describes an integrated multi-GNSS/INS system — developed and tested in both a car and on board a small quadrotor — that has been designed to achieve sufficiently accurate position and attitude control using lightweight and ultra low-cost components so as to be suitable for the technological and commercial aspects of the vehicle.

The architecture combines the advantages of absolute satellite-based positioning with the high dynamic performance and data rates of inertial sensors. The article will describe the system architecture, its carrier phase–based methodology for positioning and attitude determination, and an evaluation of the system’s performance of achieved results during real-time tests.

System Architecture Definition
Figure 1 shows a simplified block diagram of the developed on-board unit. The sensor package is composed of four non-professional, non-dedicated commercial-off-the-shelf (COTS) GNSS chipsets connected to one patch antenna each and attached to the tips of an ad hoc cross-shaped structure fixed to the quadrotor body. A motion-grade COTS MEMS IMU placed near the center of mass of the UAV measures angular rates and accelerations.

The main goal of the final system is to achieve a compact and cost-effective real-time position and attitude estimator, which is the reason why the components employed are relatively inexpensive compared to readily available platforms with costlier and bulkier elements. Therefore, an important investment of effort has been made in the system integration and in the development of specific techniques and algorithms to achieve high performance () with the combination of devices of individually moderate accuracy.

These low-cost receivers provide information to the “Processing Unit” (PU) block shown in Figure 1 in the form of so-called raw measurements, that is, basic GNSS signal-in-space data such as pseudoranges or carrier phase readings, in addition to standard position, velocity, and time (PVT) outputs. These outputs, along with the readings produced by the IMU (and possibly other sensors) are processed in the PU by a real-time microprocessor-based platform, which gathers and synchronizes these data and applies the fusion algorithms to compute the PVT and attitude (PVTA) of the platform. This PVTA estimator works on top of the standard vehicle control unit of the UAV.

The system is designed to provide high-accuracy attitude performance based on precise GNSS carrier phase measurements, reducing the position errors by combining measurements from several receivers. Furthermore, the aid of the IMU allows the high data output rate necessary for active attitude control and increases the reliability of the GNSS based attitude solution when its information is used in the ambiguity resolution process.

In the following sections we present an overview of the main functionalities developed within the processing unit, discussing their design principles and the criticisms associated with their practical implementation.

The PVTA Processing Unit
Figure 2 illustrates the architecture of the proposed navigation system (PVTA estimator) and the custom board hosting it, developed by Acorde Technologies, S.A., which is based on an ARM9 microcontroller.

This works at a clock frequency of 400 megahertz, includes two separate data and instruction cache memories of 32 kilobytes each, and two high performance sets of ROM and RAM memories of 64 and 32 kilobytes, respectively. Additionally, the board includes a SDRAM controller to interface external memory, addressable linearly up to 64 megabytes, and allowing back switching with eight-chip selects. Moreover, QNX has been selected as the real-time operating system that offers specific support for this platform and can be easily run on the board without needing to adapt it to the particular hardware setup.

Sensor Synchronization
Low-cost GNSS receivers, IMUs, and other sensors generate data in principle asynchronously with respect to each other. GNSS receivers are expected to be intrinsically synchronized among each other using the internal 1PPS signal (a one-hertz pulse). The GNSS time reference can be considered absolute for sensor synchronization purposes. In our case, this one-hertz reference is also the rate at which the GNSS modules generate observables.

The selected IMU uses its own clock (nominally 100 hertz), providing sensor data samples not aligned with the GNSS time reference or even synchronized, as this internal clock is very low precision. The IMU also generates interrupt requests (IRQ) to notify the PU whenever new inertial measurements are sampled.

Figure 3 depicts the adopted synchronization scheme, which is based on two conceptual modules (the host and the synchronization hardware [SHW]), both residing in the PU. The general time reference is given by the 1PPS signal from the base GNSS receiver, which is followed by all four GNSS modules sending their respective observables to the “host” with a small latency on the order of a few milliseconds. The GNSS data is also used to obtain the integer number of GPS seconds to which the last one-hertz pulse and subsequent carrier/pseudorange measurements belong.

The SHW includes a timer module or counter N_CNT. This timer is aligned with every 1PPS cycle and runs at one megahertz (f_CNT). Consequently the output of this timer is modulo-f_CNT. Between pulses this counter runs using its own reference, but the periodic alignment removes any possible drift as long as GNSS updates are present.

The IMU generates an IRQ with each new set of data, triggering the SHW to immediately store the value of N_CNT, thus allowing a time accuracy on the order of 1/f_CNT. The SHW then reads the data from the IMU and forwards this information to the PU with N_CNT, which serves as the timestamp of the measurements. The combination of the GNSS seconds and the GNSS-synchronized one-megahertz counter allows the tagging of inertial measurements with absolute time values within plus/minus one microsecond.

The misalignment between IMU IRQs and GNSS observables is solved via extrapolation: the microcontroller keeps a 100-hertz timer synchronized with GNSS time to generate an estimation of the inertial inputs using the most up-to-date measurements from the IMU (which runs at nearly 100 hertz). This way there are always exactly 100 inertial samples for every one-hertz pulse.

In practice, both the SHW module and synchronization host are elements of the same microcontroller unit. Several integrated hardware modules within this controller, with their corresponding drivers, take care of the timing tasks to provide an abstraction layer so that the fusion application only sees perfectly aligned 100-hertz and 1-hertz data.

Attitude Estimation by Using GNSS Carrier-Phase Measurements
The problem of accurately estimating the vehicle attitude using low-cost and lightweight sensors is resolved assuming an interferometric approach applied to the four GNSS antennas precisely mounted on the cross-shaped support. Carrier phase measurements are used to produce highly precise relative readings from the GNSS receivers. Indeed, the carrier phase is the most precise positioning resource obtainable from a GNSS signal.

The attitude of the vehicle is determined using the relative positions of the antennas; if the relative position between two antennas is known, yaw angle can be solved. With the relative position of three antennas forming a plane, the 3D attitude can also be determined. In this case, a fourth antenna is used to increase the accuracy of the solution.

Although GNSS receivers can measure the fractional carrier phase with millimetric precision, the number of wavelengths from the receiver to the satellite is unknown, a factor commonly known as integer ambiguity. To resolve the relative position between each pair of antennas, this ambiguity must be fixed.

Once all the phase ambiguities are resolved correctly, accurate relative positioning at the centimeter-level will be readily achievable using at least four satellites.

A common way to solve these ambiguities is the differencing technique, with a single phase difference between receivers expressed as:

ΔΦⁱ_αβ = Δρⁱ_α + Δdρⁱ_α – c · dT_α + λΔΝⁱ_αβ – Δd_ion + Δd_trop + Δε(Φ)    (1)

Assuming an interferometric model as depicted in Figure 4, the single phase difference between receivers α and β (e.g., α = 1 and β = 2) tracking satellite i can be formed in order to eliminate the orbital errors and, in the case of short baselines (0.5 meter baseline here), the spatially correlated ionospheric and troposheric errors as well. The ambiguity term is also differenced (λΔΝⁱ_αβ). In the case of low-cost receivers without a common clock, the receiver clock error needs to be eliminated by double differencing two single differences related to two different satellites.

The remaining error term — containing effects such as multipath or receiver errors — is doubled in the worst of cases as a consequence of double differencing. Multipath errors depend on the reflecting environment and cannot be avoided, although in high-end antennas the use of ground planes. On the other hand, the undifferenced carrier phase receiver noise is usually less than one millimeter, so that the combined receiver error on double differences is usually less than two millimeters in modern GNSS receivers.

Undifferenced phase measurements must be extrapolated to the time of the reference receiver before forming the differences. However, carrier phase measurement is also affected by Doppler shifts, produced by the relative motion of the satellites and the GNSS antennas. Thus, the phase extrapolation scheme employs the Doppler shift information and clock offsets to compensate for errors caused by the Doppler effect (which still remains after double differences as the shift varies from one instant of time to another). Extrapolation solves the lack of a clock steering mechanism in low-cost receivers.

Once the ambiguities are solved, using the approach discussed in the next section, carrier phase double differences are formed to feed a tightly coupled GPS/INS architecture as part of the measurement update.

Ambiguity Resolution Algorithm
As illustrated by Figure 5, the ambiguity resolution algorithm processes data provided by two different sources: the GNSS receivers and the last attitude information provided by the “Kalman Filter” block. From the GNSS receivers, the algorithm uses the following information:

• coordinates of the antenna of the main receiver (latitude, longitude, height) at one hertz
• ephemeris of satellites in view (obtained from the main receiver) when available
• phase, Doppler, GPS time, and clock offsets from the four receivers at one hertz.

Furthermore, it exploits the fact that, considering Figure 4, λΔΝⁱ_αβ can never exceed the number of wavelengths that fit in one baseline. Also, once the algorithm has fixed a solution, it uses the last time-updated attitude information (roll, pitch, and yaw, and their related variances) to define the search space of the ambiguities.

The ambiguity resolution algorithm is divided into several functional parts and two different strategies are envisaged, depending on the operational scenario, to provide a batch solution (when no a priori information of the attitude is known) or an on-the-fly solution (when the Kalman filter outputs are available).

Ephemeris and latitude, longitude, and height (LLH) global coordinates from the reference receiver are used to form the unitary line-of-sight vectors from the reference antenna to the GPS satellites. Previously, this information has been used to exclude low-elevation satellites from the position calculation. Doppler shifts, carrier phases, and clock offsets are used to extrapolate carrier phases to the time of the reference receiver before forming double differences.

The Kalman filter provides the “measurement update” of the roll, pitch, and yaw angles and their estimated residuals. Then the ambiguity search space is defined by using the concept of guessed baselines that will be explained in the following sections.

From the synchronized ambiguous carrier-phase double differences and the search space definition, the baseline computation of all the candidate ambiguity solutions can start. This consists of testing individual (each baseline separately) and combined (combination of three individual baseline candidates, b₁–b₂–b₃ solutions. When only one solution remains after the tests have been accomplished, ambiguities are considered to have been solved on the fly and can be provided at the PVT update rate. When the correct solution cannot be distinguished from the others, the solved carrier phase cannot be provided, thus the batch algorithm must be invoked.

The batch algorithm is based on a test over the accumulated carrier phase residuals instead of the instantaneous ones. Consequently, several epochs are required to reach the solution for the integer ambiguities.

Ambiguity Search Space Definition
The search space definition is made following the concept of “guessed baseline” as described in the article by L. Baroni and H. Koiti listed in the Additional Resources section near the end of this article. The basic idea is to search for integer combinations derived from guessed baselines instead of searching for all the integer combinations. This way, baseline-configuration geometry information can be used as a constraint to reduce the ambiguity search space to a set of ambiguity combinations that produce coherent antenna positions.

If the baseline length is fixed and known, as in the present case, only a maximum number of integer cycles can fit between antennas. This number is reached when the baseline is rotated parallel to the satellite’s line of sight vector. The maximum number of integers is calculated as the baseline length divided by the carrier wavelength, rounded downwards, thus:

Equation (2) (see inset photo, above right)

The baseline can be rotated 180 degrees with the integer being negative, or can be at right angles with the satellite’s line of sight in such a way that the integer could be zero. In this way, the number of ambiguity candidates comes down to 2N_max+1. Not all combinations of integers are possible when combining several satellites, but if a brute force algorithm was used, the number of possible integers would be (2N_max+1)^p with p being the number of ambiguities.

Attitude information from the inertial sensors combined with its predicted accuracy can be used to reduce the three-dimensional search space containing the remote antennas. Figure 6 shows an example of this.

The guessed baselines are generated in such a way that they cover the whole attitude range, while they are equally spaced. The angular step θ between two baselines is the angle that gives, at most, a whole wavelength of phase difference from one baseline to the other for any given satellite direction. To compute the individual baselines a least squares approach has been chosen due to its reliability and the reduced number of ambiguity candidates expected. Equation (3) represents the least squares problem:

Hb = ∇ΔΦ – λ∇ΔN – ε(Δφ)   (3)

where H consists of double-differenced LOS vectors, b is the baseline, ∇ΔΦ represents the carrier phase double differences, and ε(Δφ) is the residual double difference (DD) noise.

To obtain the coordinates of the individual baseline, the over-determined equation system must be solved in the following way:

b = (H^TH)^-1H^T(∇Δφ – λ∇ΔN)   (4)

At this point a sequence of tests must be undertaken to reduce the number of candidates to one for each baseline. First, a test of residuals is executed. The phase residuals are defined as the difference between the carrier phase difference measured by two antennas/receivers forming a baseline and the computed phase difference derived from the baseline vector estimation and satellite line-of-sight vectors. In formulas, the phase residuals can be defined as:

V = —HB + ∇ΔΦ + λ∇ΔN   (5)

where V is the vector containing the phase residuals, H consists of double-differenced line-of-sight vectors, b is the baseline, and ∇ΔΦ represents the carrier phase double differences.

Errors in double-difference carrier phase observations from a multi-antenna system mainly arise from multipath effects and the receiver noise. Under favorable conditions when multipath is low, the double-differenced carrier phase residuals generally exhibit a Chi-square distribution (sum of squares of independent random observations having a standard Gaussian distribution). Based on this observation, the quantification of the agreement between measured and computed observations can be made using the quadratic form of residuals:

V^TC^-1_obsV ≤ x²_ƒ,1-α   (6)

Where x²_ƒ,1-α is the Chi-square percentile corresponding to the degrees of freedom f (equal to the number of satellites minus four) and the confidence level 1 – α. C_obs is the covariance matrix of the observations. When the ambiguity candidate fails a test, the tested solution is discarded and removed from the list of candidates.

At this point the baseline geometry test is invoked. A parameter K, selected by the designer, represents the array size of the surviving candidates (i.e., the ones that provide the best results after the geometry test is executed) with respect to the whole search space.

In real conditions, K best solutions for each baseline should be considered to avoid discarding the correct solutions, i.e., retaining only the K solutions of each list of baseline candidates {b₁^(K),b₂^(K),b₃^(K)} with smaller baseline length errors obtained in the test of residuals. Then, these K best solutions are combined by means of the baseline geometry test which generates a list of baseline combinations rank-ordered by an associated error, which is obtained as a function of baseline length and known distances between baselines.

The geometry test takes advantage of the knowledge of not only the baseline lengths but also the relative position and orientation of the baselines. As mentioned earlier, the baseline geometry test exploits the fact that the baselines are not actually independent of each other, as their body coordinates are considered fixed and accurately known. The list of baseline-combination candidates is shortened depending on the obtained error value, which keeps the lower-error candidates on top of the list. Finally, the first- and second-best candidates are used to compute an error ratio in order to ensure that the selected solution can be trusted:

Equation (7) (see inset photo, above right, for equations)

Once the baselines are computed, attitude can be solved for by minimizing the function

Equation (8)

where I_i is the actual i baseline coordinates in the body frame and b_i represents the corresponding vector coordinates in the local frame. The a_i terms are weight factors, and c is the unknown rotation matrix, which transforms vector coordinates from the local to the body frame. The c matrix that minimizes L(C) can be found using Davenport’s q-method.

Batch Solution
The batch solution is based on the accumulated phase residuals test, which calls for storing the phase residuals that belong to the more likely solutions from several epochs. This test is accomplished after a configurable amount of seconds of processing and storing of data. This accumulation phase is required to observe GNSS signals within a different geometric environment in an attempt to reduce the influence of multipath.

In the following example, the angular search space has been defined as a ±30-degree search for roll and pitch (whose initial values are provided by the MEMS INS) and a full (0–360-degree) search for the yaw angle. In these conditions, the latter angle is completely unknown for a low-cost IMU. The angular steps chosen are 10, 10, and 7 degrees for baselines 1, 2, and 3, respectively. The number of possible baseline solutions is reduced after residuals and baseline length tests, as detailed in Table 1.

After completion of the geometry test, the candidate that shows the lowest error is selected as the “correct” solution and the algorithm can switch into “On-the- Fly” mode.

On-the-Fly Solution & Attitude Results
The algorithm for on-the-fly (OTF) ambiguity resolution is intended for use under dynamic conditions and is initiated with fixed ambiguities obtained from the “batch algorithm.” Its goal is to determine the correct set of ambiguities in the shortest period of time and with a minimum of computations, using single-epoch phase measurements.

Figure 7 shows an example of the effect of solved ambiguities for a static data collection for each baseline. In particular, it shows roll, pitch, and yaw angles computed from solved baseline vectors during a static data collection and by means of the batch solution, accumulating residuals during 10 epochs and using six satellites for the computation (so that five ambiguities are obtained: N1, N2, N3, N4, N5). The computed solution is compared with the one obtained from a GNSS reference receiver.

From Figure 7 we can appreciate how our solution in the attitude estimation is quite good, in particular for the heading angle, and differs by only a few degrees for the pitch and roll angles with respect to the reference solution.

On the other hand, Figure 7 shows the roll, pitch, and yaw angles computed from solved baseline vectors, during a static data collection and by means of the OTF solution, using again six satellites and one epoch. As expected, we can see that the OTF solution generates a more noisy solution compared with the results obtained with the batch solution. The attitude data computed through the multi-GNSS antenna platform are then integrated with the INS according to a tightly coupled technique. Details will be explained in the next section.

Navigation Solution Determination
Figure 8 reports the complete block diagram of the proposed navigation system, hosted in the PU. Even if only three GPS receivers are in principle sufficient to have attitude estimation, our choice to use four GNSS devices is made to provide additional redundancy and robustness against partial GPS outage or physical failure of one of the receivers. Double difference measurements, ephemeris, and estimated attitude are fed into the Tightly-Coupled Algorithm at a rate equal to 1 Hz.

The selected low-cost IMU provides three accelerometers and three gyro measurements at a nominal rate of 100 Hz. They are low-pass filtered to mitigate the mechanical vibrations of the UAV, then are used to compute the INS navigation solution according to strapdown mechanization equations described in the article by D. H. Titterton and J. L. Weston cited in Additional Resources.

An efficient real-time implementation of the strapdown inertial navigation algorithm requires the splitting of the computing processes into low- and high-speed segments. The low-speed calculations are designed to take into account low-frequency, large-amplitude body motions arising from vehicle maneuvers. These are used to determine attitude, velocity, and position, whilst the high-speed section involves a relatively simple algorithm designed to keep track of the high-frequency, low-amplitude motions of the vehicle (i.e., coning and sculling computations). See the articles by P. G. Savage in Additional Resources for more details. We have chosen a computation rate for the high-speed segment equal to 100 hertz while the low-speed portion has a rate that can range from 10 to 20 hertz.

The INS solution is blended with the GPS information in an extended Kalman filter (EKF) according to a tightly-coupled method. This filter estimates the navigation solution and the INS errors by using the following parameters as input:

• ranges, attitude, and Doppler outputs computed by the INS device
• Doppler frequency estimated by the GPS base receiver
• satellites’ position and velocity
• double-difference carrier-phase measurements
• estimated attitude from the four GNSS receivers.

The EKF incorporates a 17-state error model that includes position error, velocity error, and attitude error, accelerometer bias, gyroscope bias, clock bias, and clock drift errors, represented as follows:

Equation (9)

Equation (10)

Equation (11)

where:

• H[n] is the Jacobian matrix of the non-linear relationship between the user position and clock and the N_sat pseudoranges ρ₁, …, ρ_Nsat. A detailed explanation of H[n] can be found in the Ph.D. thesis by M. Petovello (Additional Resources).
• H_yaw[n] is the measurement design matrix for external heading measurements and can thus be written as given in G. Falco et alia 2013;.
• H_DD[n] is the design matrix related to the DD carrier-phase measurement for each baseline, which depends on H[n] and the lever-arm effect. (For a complete formulation, see the article by Y. Yang et alia in Additional Resources).

Observing the expression of H[n], it appears that the DD phase measurements are used to improve the attitude resolution only, not the position accuracy. This is by no means a conceptual limitation, and DD measurements could be similarly added as observations to the current tightly coupled position solution algorithm.

Nevertheless, the main innovation of the new tight-coupling algorithm implemented in our navigation solution is in the use of the DD measurements that are given as input to improve the estimation of the attitude.

Field Tests Results
Our algorithm design took computational complexity into consideration to allow real-time operation in a low-cost, low-power microcontroller. The code has been implemented in C language and is able to run in real time with room for further optimization. It also permits the option of running additional firmware on top of the navigation core, such as a possible integration of control algorithms in the same platform. More details about the firmware design can be found in G. Falco et alia 2014.

We first tested and validated the hardware and software of the developed navigation system on a car and then mounted on board the target UAV. We used two antenna arrays for the land applications two antenna arrays: The first consists of four low-cost GNSS antennas with a relative distance of 50 centimeters, while the second set is formed by professional-grade antennas with stable phase centers and connected to a professional reference receiver. Figure 9 shows the test equipment and the various settings of the GNSS antennas.

Attitude accuracy tests were conducted first in an open-sky situation, then in a challenging urban scenario. Hereafter, we show the results obtained during a drive in downtown Santander, Spain, the whole trace of which is shown in Figure 10. Narrow streets, boulevards, and the presence of high buildings characterize that urban environment, which affected the correct reception of the satellite signals. Moreover, in such a scenario, the DD carrier phase resolution becomes very difficult to achieve, and often attitude must be estimated by using only the IMU information.

Figure 11 depicts the three Euler angles for yaw, pitch, and roll during the test drive. As reflected in the figure, we can only compare the two attitude estimations at a limited number of points because the receiver often experienced time instants where the three Euler angles were not available. In the part of the trajectory where we can measure such angles, we can observe the error in the INS output of pitch and roll with mean errors of 0.5 and 0.8 degrees, respectively. In comparison, the yaw has a mean error that is slightly bigger the one degree.

After having validated the performance of the system on a land vehicle, we further tested it on board the target UAV. The selected end-user application is a four-rotor rotary wing UAV named Anteos (See Figure 12).

The integration within the UAV required three different integration activities board electrical/mechanical, GNSS antennas mechanical, and software integration.

The mechanical integration of the navigation system processor required the board to be free of most of the vibration that a UAV can generate. For this reason, the board was mounted on the payload docking bay of the UAV. The board is aligned with the UAV INS (namely, an attitude/heading reference system, or AHRS) to obtain directly comparable measurements.

The electrical integration consisted of providing the main batteries voltage to a switching power supply.

The integration of the GNSS antenna required a specific carbon fiber structure prototype to be mounted on top of the UAV, allowing the required 50-centimeter baseline between the antennas. Special care was taken to decrease vibration and the flexion of the structure as much as possible during UAV flights. Moreover compared to other low cost antennas, the chosen unit presents an excellent trade-off between minimization of phase center variation and antenna compactness.

On the UAV side, a serial line has been dedicated to communication with the platform. The purpose of this communication is to gather real time measurements from the navigation system and incorporate them on the UAV real-time telemetry daemon, a portion of the UAV control code dedicated to logging telemetries both for instantaneous use within the UAV control law, and for the logging and post-processing of the mission data.

The flight tests compared the measures taken from the new navigation platform and the on-board INS alone, allowing a real-time comparison of the position/attitude solutions taken from the two independent units. As an example, in Figure 13 the latitude and longitude for both units have been converted in the planar displacement with respect to a common point in order to compare the results in terms of meters. The depicted test was taken from motors power on (i.e., around 40 seconds on ground with UAV motors on) and then about 200 seconds of flight.

In Figure 14 we have plotted the attitude solution obtained with our navigation system compared with the reference one.

The integration showed the capability of the system components to be easily combined and to provide accurate measurements on a demanding platform such as a rotary-wing UAV (no preferred directions, no clamping on ground, side movements, strong electromagnetic fields induced by the four electric motors, vibrations, high dynamics). Once the UAV is in flight the general trend of the measurements follows those of the UAV’s INS, even though at some points the reported attitude differs by some degrees from that of the UAV, which would lead the attitude controller onboard the UAV to overreact.

Conclusions
We designed a sophisticated real-time navigation solution that exploits information coming from multiple GPS receivers and a low-cost MEMS IMU. We were able to estimate the attitude of a UAV platform by forming double-difference carrier-phase measurements to feed a tightly coupled GPS/INS integration architecture. In this way, we demonstrated the technical feasibility of an accurate, low-cost navigation system using non-dedicated hardware and its potential application for UAV navigation.

Acknowledgment
The presented results have been achieved within the LOGAM project which has been funded by the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement #277663.

Additional Resources
[1] Acorde Technologies, S.A., website
[2] Aermatica SPA, website
[3] Baroni, L., and H. Koiti, “Analysis of Attitude Determination Methods Using GPS Carrier Phase Measurements,” in Mathematical Problems in Engineering, Vol. 2012, Hindawi Publishing Corporation, Article ID 596396, p. 10, doi:10.1155/2012/596396, 2012
[4] Falco, G., and M. Campo-Cossío Gutiérrez, E. López Serna, F. Zacchello, and S. Bories, “Low-cost Real-time Tightly-coupled GNSS/INS Navigation System Based on Carrier Phase Double Differences for UAV Applications,” Proceedings of the 27th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+2014), Tampa, Florida USA, pp:841-857, doi: 10.13140/2.1.4638.4642, 2014
[5] Falco, G., and M-C. Cossio and A.Puras, “MULTIGNSS Receivers/IMU System Aimed at the Design of a Heading-Constrained Tightly-Coupled Algorithm,” Proceedings of the International Conference on Localization and GNSS (ICL-GNSS 2013), Turin, Italy, doi: 10.1109/ICL-GNSS.2013.6577263, 2013
[6] Mehut. Y., and C. Delaveaud and S. Bories, “Low-Cost GNSS Antennas Phase Center Variations Characterization for UAV Attitude Determination Application,” Proceedings of AMTA 2013 35th Symposium, Colombus, Ohio USA, October 7–10, 2013
[7] Petovello, M., Real-time Integration of a Tactical-Grade IMU and GPS for High-Accuracy Positioning and Navigation, Ph.D. Thesis, Department of Geomatics Engineering, University of Calgary, Canada, UCGE Report No. 20173
[8] Savage, P. G., “Strapdown Inertial Navigation Integration Algorithm Design Part 1: Attitude Algorithms,” Journal of Guidance, Control and Dynamics, Volume: 21, Number: 1, pp. 19 – 28, 1998
[9] Savage, P. G., “Strapdown Inertial Navigation Integration Algorithm Design Part 2: Velocity and Position Algorithms,” Journal of Guidance, Control and Dynamics, Volume: 21, Number: 2, pp. 208- 221, 1998
[10] Strang, G., and K. Borre, Linear Algebra, Geodesy and GPS, Willesley-Cambridge Press, ISBN-10: 0961408863
[11] Titterton, D. H., and J. L. Weston, Strapdown Inertial Navigation Technology, 2nd ed., Paul Zarchan, Editor, ISBN:1-56347-693-, 1997
[12] Wei, Z., “Positioning with NAVSTAR, the Global Positioning System,” Report No. 370, Department of Geodetic Science and Surveying, The Ohio State University, Columbus, Ohio USA, 1986
[13] Yang, Y., and A. Farrel, “Two Antennas GPSAided INS for Attitude Determination,” IEEE Transactions On Control Systems Technology, Volume: 11, Number: 6, pp. 905-918, doi: 10.1109/ TCST.2003.815545, 2003

The post Thinking Small 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.

Modern global navigation satellite systems have adopted binary offset carrier (BOC) modulations to increase radio frequency (RF) compatibility among different signals and to improve ranging accuracy. BOC modulations use an additional signal component, the subcarrier, to move the signal power away from the signal center frequency and to obtain two main lobes displaced by f_sub, the subcarrier repetition frequency.

The subcarrier significantly reduces interference issues and leads to signals with sharp autocorrelation functions, i.e., with improved ranging capabilities. BOC signals are, however, characterized by multi-peaked correlation functions, and, as a result, secondary peak lock can occur. Thus, several techniques have been designed to avoid secondary peak lock and to track unambiguously the main signal correlation peak. The design of unambiguous BOC tracking algorithms has been strongly influenced by the perception of the subcarrier, which evolved significantly over the last two decades.

This article will first review the various perceptions of the BOC subcarrier and then describe a new view of the subcarrier along with an advanced tracking algorithm that exploits this subcarrier concept to fully benefit from the structure of BOC modulated signals.

Subcarrier Perception
Modern GNSS signals can be modeled as the product of four components:

where

x(t) = d(t)c(t)s_b(t)cos(2πf_RFt)    (1)

• d(t) is the navigation message containing the ephemerides and other navigation parameters
• c(t) is a pseudorandom sequence selected from a family of quasi-orthogonal codes. c(t) is binary phase shift keying (BPSK)–modulated, i.e., each element of the code is represented as a constant (positive or negative) value.
• s_b(t) is the subcarrier obtained by periodically repeating a basic waveform
• the cosine term is the carrier which is used to up-convert the signal to the RF, f_RF.

Figure 1 provides a schematic representation of the various signal components. (The figure ignores the navigation message, which slowly varies over time).

The code and carrier components are also present in legacy GPS signals and are usually processed using dedicated tracking loops: the delay lock loop (DLL) and the phase lock loop (PLL). The subcarrier has been introduced with the advent of Galileo (e.g., Open Service signal) and the modernization of GPS (e.g., L-band civil signal, L1C) to improve RF compatibility among different GNSS signals and increase ranging accuracy. (See the article by J. W. Betz, referenced in the Additional Resources section near the end of this article.)

The subcarrier has been considered in various ways by researchers and GNSS receiver designers, and its perception has been progressively changing over time. The subcarrier has been originally recognized as part of the ranging code, c(t), and jointly processed with it using a modified DLL. When considered in this way, the subcarrier modifies the code correlation function by leading to a narrower main correlation peak and by introducing secondary peaks.

Secondary peaks can be the source of ambiguity, and a standard DLL can erroneously lock on them, leading to biased pseudorange measurements. For this reason, one class of BOC tracking algorithms aimed to remove such ambiguity, for example, by introducing sentinel correlators as in the “Bump Jump” method detailed in the paper by P. Fine and W. Wilson, cited in Additional Resources. The sentinel correlators are used to verify that the peak locked by the DLL is the main one.

The subcarrier was then perceived as a nuisance component to be removed. In order to remove the subcarrier, several techniques were suggested such as the “SubCarrier Cancellation (SCC)” method described by P. Ward et alia (Additional Resources), side-band processing, and subcarrier pre-filtering. Although subcarrier removal can lead to robust signal tracking, the advantages brought by the subcarrier are generally lost in the process. In particular, these techniques usually lead to losses in terms of measurements accuracy, as they are unable to obtain code correlation functions with a narrow peak.

A new view of the subcarrier implies that the subcarrier should be considered similarly to the signal code and carrier in which a dedicated tracking loop is allocated to each component, including the subcarrier. Moreover, this approach suggests that the subcarrier should be exploited as a source of measurements in the same way as the code is used to generate pseudoranges and the carrier to produce carrier phase observations. For this reason, the subcarrier would also be used to generate subcarrier phase/delay measurements. Further, the subcarrier has characteristics intermediate between the code and carrier and, thus, can be processed by modifying techniques originally designed for these two signal components.

A fundamental step towards such a concept of the subcarrier was the “Double Estimator (DE)” suggested by M.S. Hodgart et alia (in this article) where the code and subcarrier are processed independently. In particular, the subcarrier is processed similarly to the code using a dedicated loop, the “Subcarrier Lock Loop” (SLL).

This method introduces subcarrier “Early” and “Late” correlators and uses these to track the subcarrier component. Moreover, this approach employs the concept of “subcarrier delay,” but only for tracking purposes. The subcarrier is not used for generating measurements, and code and subcarrier delays are recombined in order to generate pseudorange observations.

The DE method assimilates the subcarrier to an additional code superimposed through a multiplication of the BPSK-modulated ranging code. At the same time, a progressive understanding has emerged that the subcarrier is also similar to the carrier component. In the SCC method, two orthogonal sine waves are used as local subcarriers for the removal of the subcarrier component. In his Ph.D. thesis (see Additional Resources), C. Palestini used the subcarrier delay to smooth code measurements using a Hatch filter. J. Wendel and S. Hager (cited in Addition Resources) solved the subcarrier ambiguity problem using the LAMBDA method. These are approaches typically adopted for processing carrier phase measurements. The subcarrier is thus implicitly recognized as a source of measurements.

The “Double Phase Estimator (DPE)” described in the next section exploits this new conception of the subcarrier, which is approximated as a pure sinusoid and processed using a modified PLL, the “Subcarrier Phase Lock Loop (SPLL).”

The Double Phase Estimator
The DPE exploits the commonalities between carrier and subcarrier. Moreover, it takes into account the effects of the receiver front-end. In particular, Equation (1) models the transmitted GNSS signal but does not consider several propagation and reception effects, such as those caused by the receiver front-end.

In an article from the author listed in Additional Resources, it is shown that, in the presence of front-end filtering, the subcarrier of the received signal can be effectively approximated as a pure sinusoid. When assimilated as a pure sinusoid, the subcarrier can then be tracked using a modified PLL, the SPLL, which exploits the correlation of the input signal with two orthogonal local sinusoids.

Figure 2 provides a schematic representation of the DPE. The signal at the input of the DPE is denoted by y[n]. Note that y[n] is a digital sequence obtained by sampling a filtered and down-converted version of the signal recovered by the receiver antenna. The residual signal Doppler effect is at first removed using the complex exponential generated by the PLL used to track the signal carrier. Code and subcarrier components are then processed independently using a standard DLL and a SPLL, respectively.

The SPLL uses an additional correlator, denoted as quadrature prompt correlator, to estimate the residual subcarrier phase error. This correlator is obtained by correlating the input signal with a local replica orthogonal to the input signal subcarrier. This orthogonal subcarrier is obtained by delaying by T_sub/4 the standard subcarrier used for the evaluation of the standard prompt correlator.

Equation (a) (see inset photo, above right, for equations)

is the subcarrier period.

In Figure 2, the standard subcarrier appears as a sine wave, whereas the orthogonal subcarrier is a cosine wave. This choice is dictated by the fact that a sine-phased BOC modulation is considered in Figure 2. When a cosine-phased modulation is processed, cosine and sine waves should be adopted for the generation of standard and quadrature components, respectively.

In Figure 2, the symbol N represents the number of samples used for the signal correlation, and F_c(z) and F_sb(z) denote the transfer functions of the filters adopted by the DLL and the SPLL, respectively. In equations (2) and (3), the symbols P and P_Q are used to denote the standard and quadrature prompt correlators. Two subcarrier discriminators can be used to extract the residual subcarrier delay error, as follows:

Equation (2)
Equation (3)

Discriminator (2) is sensitive to residual phase errors from the PLL whereas (3) is non-coherent and can operate in the presence of residual phase errors. Δτ_s is the residual subcarrier delay error, which leads to the discriminator outputs, ε_c and ε_nc, used to drive the SPLL.

Performance Analysis
In the DPE, the local subcarrier is a pure sinusoid. When a wideband front-end is used, the subcarrier of the received signal can be effectively approximated as a square wave. Under these conditions, a mismatch between received and local subcarrier could occur, which can introduce a signal-to-noise ratio (SNR) loss. In the worst case, this loss is equal to

Equation (4)

The SNR considered here is the coherent output SNR evaluated at the output of the prompt correlator. This SNR loss is present only in the absence of front-end filtering. An expression for the SNR loss (L₀) can be found in the article by author cited in Additional Resources. L₀ can become a gain when the signal is heavily filtered and the subcarrier of the received signal is more similar to a pure sinusoid than to an ideal square wave.

This fact clearly emerges in Figure 3 where the SNR loss is shown as a function of the front-end bandwidth. More specifically, a 9th-order Butterworth filter was used to simulate the effects of front-end filtering, and L₀ was evaluated as a function of its cut-off frequency. Figure 3 considers the cases of sine BOC(1, 1) and cosine BOC(15, 2.5) and reveals that the DPE provides a gain for the processing of wide-band BOC–modulated signals. This is the case of the cosine BOC(15, 2.5) modulation adopted for the Galileo Public Regulated Service (PRS) signal: a front-end with a total bandwidth greater than 45 megahertz is required to obtain an actual loss.

Note that signal band-limiting can also be introduced on the transmitter side. For example, the cosine BOC(15, 2.5) transmitted by the GIOVE-B satellite was characterized by a bandwidth of about 40 megahertz. In this case, the DPE will always provide better performance with respect to standard techniques.

The use of pure sinusoids as local subcarriers also affects the correlation functions evaluated by the receiver. In particular, in the DE and in the DPE techniques, a two-dimensional crosscorrelation function is obtained:

Equation (5)

Note that these two components are delayed independently by τ_c and τ_s, the code and subcarrier delays tested by the receiver. The symbol ‘-’ has been introduced on the local subcarrier to indicate that a sequence different from the signal subcarrier, s_b(•), can be used for the computation of cross-correlation (5). The DE uses a subcarrier equal to that of the input signal, whereas the DE uses sinusoidal subcarriers.

From the two-dimensional cross-correlation, it is possible to extract the following:

• the code correlation, when the subcarrier delay, τ_s, is matched to that of the incoming signal y[n]
• the subcarrier correlation, when the code delay, τ_c, is matched to that of the incoming signal y[n]
• the composite correlation, when the code and subcarrier delays are constrained to be equal, τ_c = τ_s. This is the correlation obtained by standard BOC tracking algorithms.

The following discussion only considers the subcarrier and composite correlations.

When a wideband front-end is used, the DE and standard BOC tracking algorithms are able to obtain sharp cross-correlation functions close to the “Ideal” curves depicted in Figure 4. In the presence of front-end filtering, the correlation functions obtained using square waves as local subcarrier are strongly impacted. This fact clearly emerges from Figure 4, which shows in the top and bottom panels, respectively, the composite and subcarrier correlation functions of a cosine BOC(15, 2.5) signal in the presence of front-end filtering. Signal band-limiting has been simulated by filtering the input signal with a 9th-order Butterworth filter and cut-off frequency f₀ = 40 MHz.

In Figure 4, the curves denoted as “Ideal” and “Sin” are those obtained for the DE (square subcarrier wave) and for the DPE in the absence of filtering. When filtering is simulated, the correlation function computed using standard techniques are smoothed (“Filtered” curves in Figure 4). No significant difference can be observed for the DPE: Because only the first frequency component is retained, signal band-limiting has almost no effect on the correlation process, which implicitly performs filtering. The bottom part of Figure 4 reveals that when the front-end filter preserves only the main signal component, the DE and DPE operate on equivalent subcarrier correlation functions, which can be effectively modelled as pure cosine waves.

Tracking Jitter
The tracking jitter is a measure of the uncertainty of the final output provided by a tracking loop. The following discussion analyzes and compares the tracking jitter of the SPLL used in the DPE with that of the SLL of the DE. In particular, the tracking jitter can be computed as (see the book chapter by A. J. Van Dierendonck, Additional Resources):

Equation (6)

where σ_d is the standard deviation of the discriminator output, B_eq is the loop equivalent bandwidth, and T_c = NT_s is the coherent integration time. G_d is the discriminator gain defined as

Equation (7)

where S(τ) is the discriminator input-output function. S(τ) depends on the type of discriminator and can be computed using definitions (2) and (3).

The 2014 article by the author shows that the following equation gives the tracking jitter of the SPLL, for both coherent and non-coherent discriminators:

Equation (8)

The last approximation in (8) was obtained by exploiting the hypothesis that the front-end bandwidth is approximately equal to half the sampling frequency, as follows:

Equation (9)

The tracking jitter of the output of the SLL used by the DE is given by

Equation (10)

and depends on d_s, the subcarrier Early-minus-Late chip spacing. As previously mentioned, the DE treats the subcarrier similarly to the code, and two additional subcarrier correlators are used to track the main peak of the subcarrier correlation function. In Equation (10), d_s is normalized by the subcarrier period, T_sub, and thus is unitless.

Figure 5 compares the tracking jitter of the SLL with that of the SPLL. The curves shown in the figure were obtained using the parameters listed in Table 1.

The performance of the SLL strongly depends on the Early-minus-Late spacing adopted: for narrow d_s, the SLL outperforms the SPLL, which becomes preferable for d_s greater than 0.25. For d_s = 0.25, the SLL and SPLL have similar performance as predicted by (8) and (10) when

Equation (b)

The curves in Figure 5 were obtained in the least favorable case for the SPLL, i.e., in the absence of front-end filtering.

Figure 5 also provides the tracking jitter of the DLL of the SCC technique. Note that when recombining subcarrier and code measurements as in the DE, the dominant source of error stems from the SLL. Thus, we can effectively approximate the final tracking jitter using the subcarrier delay estimate. For this reason, the tracking jitter of the SCC can be compared with that of the SLL and SPLL.

Because no theoretical results are currently available for the SCC tracking jitter, Figure 5 only presents simulation results. The algorithm detailed in the article by P. Ward et alia was implemented and used for the analysis: Subcarrier Cancellation also requires the computation of Early and Late correlators for the DLL and the performance of the loop strongly depends on the Early-minus-Late chip spacing, d_s, of these two correlators.

The results provided in Figure 5 indicate that the SCC is always outperformed by the other techniques. This anticipated outcome is due to the subcarrier removal operated by the SCC technique. Although the SCC uses a sinusoidal representation of the subcarrier, its performance is significantly worse than that of the DPE.

Real Data Processing
In order to test the effectiveness of the DPE, cosine BOC(15, 2.5) signals collected from the GIOVE-B satellite were used. Note that the European Space Agency decommissioned GIOVE-B satellite in July 2012 and that the dataset used in this paper was collected on November 5, 2011. The use of such a dataset is justified by the fact that it contains valid cosine BOC(15, 2.5) data with a known pseudorandom noise (PRN) code. Thus, the use of this dataset allows one to test the DPE for both the sine BOC(1, 1) and cosine BOC(15, 2.5) modulation broadcast in the Galileo E1 band. The cosine BOC(15, 2.5) signals transmitted by the currently operating Galileo satellites are encrypted, and use of them requires codeless techniques.

A complete analysis of the tracking results obtained for the sine BOC(1, 1) signal can be found in the article from the author listed in Additional Resources. The following discussion addresses only the case of the cosine BOC(15, 2.5) modulation. In particular, the DPE is able to effectively process the E1a signal. Figure 6 provides sample results including several metrics that indicate the proper functioning of the proposed technique. Table 2 lists the parameters used for the processing of the GIOVE-B E1a signal.

Figure 6a shows the amplitudes of the Prompt, Early, and Late correlators used by the DLL, coupled with the SPLL: After an initial transient period, the amplitude of the Prompt correlator is maximized whereas Early and Late correlators assume similar magnitudes.

Figure 6b provides the filter outputs of the three loops used for signal tracking. Each output is normalized by the fundamental frequency of the component tracked: the carrier Doppler by the GPS L1 center frequency, 1575.42 MHz; the DLL filter output by the nominal code rate, 2.5575 megahertz; and the SPLL filter output by the subcarrier rate, 15.345 megahertz.

The normalized filter outputs assume similar values indicating the possibility of carrier aiding: The normalized carrier Doppler can be used to help process the code and subcarrier components. As expected the code estimates are the nosiest. After about two seconds, the secondary code on the E1a signal is recovered and bit synchronization is achieved. Thus, the integration time is increased from 2 to 10 milliseconds.

The latter effect can be clearly seen in the code rate estimates in Figure 6b and in Figure 6c, which shows the in-phase and quadrature components of the E1a Prompt correlators. After bit synchronization, the secondary code is removed and the navigation bits can be extracted from the in-phase components of the Prompt correlator.

Finally, Figure 6d compares the magnitude of the P_Q correlator with that of the Prompt correlator. After an initial transient, the magnitude of P_Q is minimized and all the signal energy is concentrated in the Prompt correlator. This shows the ability of the Double Phase Estimator to properly track the different components of the cosine BOC(15, 2.5) modulation.

Conclusions
In this article, various ways of considering the subcarrier component have been briefly reviewed. In particular, the discussion promotes the idea that the subcarrier has its own “dignity.” In this respect, the subcarrier should be processed using a dedicated tracking loop and considered as a source of measurements.

Furthermore, the subcarrier has characteristics intermediate between the code and carrier, and thus it can be processed adapting algorithms originally designed for these two signal components. The Double Phase Estimator exploits the “carrier” nature of the subcarrier, which is processed using a modified PLL, the SPLL. The DPE is an effective alternative to the DE and can achieve improved performance in the presence of front-end filtering.

Additional Resources
[1] Betz, J.W., “Binary offset carrier modulations for radionavigation,” NAVIGATION, the Journal of the Institute of Navigation, Vol. 48, No. 4, pp. 227–246, Winter 2001
[2] Borio D., “Double phase estimator: a new unambiguous binary offset carrier tracking algorithm,” IET Radar, Sonar & Navigation, vol. 8, no. 7, pp. 729–741, August 2014
[3] Fine, P., and W. Wilson, “Tracking algorithm for GPS offset carrier signal”,” Proceedings of the National Technical Meeting of the Institute of Navigation (ION NTM 1999), San Diego, CA, pp. 671–676, January 1999
[4] Hodgart, M.S., and P. Blunt and M. Unwin, “Double estimator – a New Receiver Principle for Tracking BOC signals,” Inside GNSS, Vol. 3, No. 4, pp. 26-36, Spring 2008
[5] Palestini, C., “Synchronization and Detection Techniques for Navigation and Communication Systems”, Ph.D. thesis, University of Bologna, March 2010
[6] Van Dierendonck, A. J., “GPS receivers,” in B. W. Parkinson and J. J. Spilker Jr. (editors), Global Positioning System Theory and Applications, Chapter 5, Volume 1, pp. 329–407, American Institute of Aeronautics & Astronautics 1996
[7] Ward, P. W., and W. E. Lillo, “Ambiguity Removal Method for Any GNSS Binary Offset Carrier (BOC) Modulation,” Proceedings of the International Technical Meeting of The Institute of Navigation (ION ITM 2009), pp. 406–419, Anaheim, California, January 2009
[8] Wendel, J., and S. Hager, “A robust technique for unambiguous BOC tracking”, Proceedings of the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+), Nashville, TN, pp. 1–12, September 2013

The post Double Phase Estimator appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

After three years in Chapter 11, the company whose planned wireless broadband system threatened to overload GPS receivers across the United States is preparing to emerge from bankruptcy.

Although the passage of time has done little to change the issues between Virginia-based LightSquared and the GPS community, although the context in which some of those issues will be decided has decidedly shifted. The Senate is now under Republican control, federal agencies are counting pennies, the Federal Communications Commission (FCC) has a new chairman, the law governing the FCC is being rewritten, and the value of auctioned spectrum has skyrocketed.

Perhaps most importantly, the heated fight between the company and GPS organizations in 2011 instilled a far deeper and wider understanding of satellite navigation. Decision makers who were unaware of the ubiquitous use of GPS — and, more broadly, GNSS — signals are now far more familiar with its deep integration into the nation’s economy and critical infrastructure. This awareness is reinforced daily by fresh worries over cybersecurity, signal jamming and satellite vulnerabilities.

Court: Thumbs Up
On March 26 Bankruptcy Judge Shelley C. Chapman, after considering a years-long string of proposals, approved an exit plan for the firm that will largely put control of LightSquared in the hands of its investors and pay $1.5 billion to major creditor and broadband rival Charlie Ergen, president, CEO and of chairman of the board of Dish Network. The Harbinger hedge fund, the primary backer of LightSquared, and its CEO Phil Falcone are to maintain a minority role.

It is now unclear, however, if the plan will stand.

Bloomberg reported on May 12 that Ergen had filed a challenge to the plan in court, alleging the “bankruptcy plan gives hedge funds that invested in the broadband company a leg up while blocking telecommunications firms from competing with it.”

Even if the exit plan withstands another challenge, the court’s approval was always just the first of two steps involved in emerging from bankruptcy. Light- Squared must also convince the FCC to transfer its spectrum licenses to the reconstituted company, called, appropriately enough, New LightSquared.

The transfer request, which will be published for public comment, was submitted to the FCC on April 6. As of mid- May, however, it had not appeared in the Federal Register.

Wanna Trade?
In the meantime LightSquared is waiting on a different FCC decision — this one on its proposal to modify its spectrum licenses.

The company had originally sought to repurpose frequencies allocated for relatively low-powered satellite signals to instead support a network of some 40,000 broadband ground stations. That broadband capacity was to be sold wholesale, potentially fueling an entirely new level of competition — an approach that appeared at the time to inspire strong FCC support.

LightSquared’s plan, which came fully to light late in 2010, also dovetailed neatly with a pledge made by the White House. The Obama Administration, heading into a mid-term election with a damaged economy, had promised to find 500 megahertz of spectrum for the ballooning, and hopefully job-rich, broadband sector.

The project ran aground, however, after tests confirmed that the Light- Squared signals would overload the vast majority of GPS receivers. The FCC put the project on indefinite hold in February 2012 and the firm filed for protection from its creditors three months later.

LightSquared proposed a spectrum swap in September 2012 to ameliorate some of the interference. The firm currently has licenses for two 10-megahertz bands of spectrum running from 1526 to 1536 MHz and 1545.2 to 1555.2 MHz for downlink and two 10-megahertz bands at 1627.5 to 1637.5 MHz and 1646.7 to 1656.7 MHz for uplink.

The company has offered to forego the use of the most problematic 1545.2 to 1555.2 MHz band, which is the one closest to the GPS frequencies. It would also refrain — for an unspecified amount of time — from using the other downlink band to allow GPS users to transition. In exchange LightSquared is asking to be allowed to share a five-megahertz band (1675 to 1680 MHz) used by the National Oceanic and Atmospheric Administration. The firm happens to have rights to use an adjacent five megahertz band, so it would have 10-megahertz block of frequencies to balance out its set.

Money Hits the Airwaves
The FCC has yet to decide but the practicalities of making a straight swap are murky given the current budget climate and a sudden, recent surge in spectrum values. The sequestration limits that upended federal spending in 2013 had not been implemented when the company first proposed the swap in 2012. Now, any such deal is likely to face stiffer headwinds.

The challenge was underscored in the president’s fiscal year 2016 budget, which is currently under consideration in Congress. Even though LightSquared’s proposal has been on the table for several years, the White House factored auctioning the band into its budget request, plugging $230 million in anticipated revenue into the FCC’s allocation.

And that $230 million estimate is very likely to be low.

Auction 97 for “advanced wireless services”, a spectrum auction that ended this January, underscored just how valuable every megahertz has become. The sale of paired frequency blocks, including frequencies from 1755 to 1770 MHz, drew 70 qualified bidders, and raised $41.3 billion in net bids — blowing past the preset reserve of $10 billion and more than doubling the $20 billion experts had expected.

Even if the FCC elected to forgo an auction and sell the spectrum directly to LightSquared, the price would have to be far higher than $230 million to prevent other hopeful owners with broadband aspirations from raising a stink.

“The reality is that spectrum is going to be auctioned,” said Tim Farrar of Telecom, Media and Finance Associates, Inc., an expert in satellite communications and wireless spectrum.

“When you look at what spectrum has sold for this year, it’s hard to imagine that it wouldn’t be seen as a giveaway, if they were to give that spectrum or sell that spectrum to LightSquared for $200 million,” said Farrar. “It ought to be worth at least $1 billion.”

The good news for LightSquared is that high spectrum prices could give the company some fiscal elbowroom. A valuation prepared by the investment bank Moelis in 2014, completed before Auction 97, put the value of LightSquared’s two uplink bands and the potential NOAA frequencies at $4.8 to $7.2 billion in October 2014 dollars. At the point Light- Squared can use the less troublesome of the two original downlink bands, it will add between $881 million and $1.22 billion to the company’s valuation.

The recent auction results now suggest much higher numbers and those suggested by Moelis and go a long way toward explaining the abiding interest in LightSquared’s spectrum, interference issues and all.

The numbers also put more pressure on the GPS community, which is not convinced that the latest LightSquared plan will work compatibly on a technical basis with GNSS receivers.

Cautious Comments
The organizations that posted feedback during the FCC comment period on LightSquared’s swap proposal generally expressed their support for finding more bandwidth for broadband. They were also generally deeply cautious about the proposal, particularly with regard to potential problems from having a large number of handsets operating in the two uplink bands.

The Department of Transportation (DoT), which represents non-military users of the GPS system in the National Executive Committee for Space-Based Positioning, Navigation, and Timing (PNT), underscored the worries about the handsets.

In a letter submitted through the National Telecommunications and Information Administration (NTIA), DoT noted a lack of data on the proposed handsets and assumptions about how many handsets there would be. The impact of the handsets on aviation needed to be more full assessed, the agency said, including doing analyses with different airframes and looking at handsets operating at maximum power as opposed to average power.

DoT also argued for a closer examination of assumptions made about GPS receiver antennas and an analysis of the potential adverse effects that Light- Squared user equipment on the ground or at the gate could have on aircraft either in flight and/or on the ground.

“In light of these concerns,” the letter said, “the Department questions whether the Commission has the necessary and sufficient information before it to approve the handset proposal at issue in the Public Notice. Again, to the Department’s knowledge, there has not been any robust interagency effort to examine or test LightSquared’s proposal, to probe the underlying assumptions or to consider feasible alternatives. Furthermore, at least with respect to potential GPS interference, the Department notes that this is not solely a government concern, but one in which a variety of individuals and organizations, including many in the private sector, have a stake.”

A number of those who submitted comments asked that the matter be evaluated through a full rulemaking process. An ad hoc approach was “precisely what has impeded LightSquared from providing its initially proposed service in the first instance,” wrote the GPS Innovation Alliance (GPSIA).

“Instead,” GPSIA suggested, “these issues should be considered in a broader spectrum planning process,” GPSIA suggested, “in the context of a transparent public notice-and-comment rulemaking proceeding in which established spectrum protection criteria and all relevant public policy issues can be considered to determine the parameters in which the spectrum can be safely used.”

Capitol Hill
As it happens a bill now before Congress would emphasize just that approach. The Federal Communications Commission Process Reform Act of 2015 seeks to make the FCC process more transparent. Among its many provisions are minimum comment periods — something that could have eased the initial tension between LightSquared and the GPS community when the company’s proposal was first published in 2010.

The bill (S. 421) has already been submitted to the Senate, and the House was scheduled to debate a draft of the bill, which appears to be the same as the Senate measure, as this publication was going to press.

One of the bill’s proponents is Greg Walden (R-Oregon), one of two lawmakers leading an effort to rewrite the Telecommunications Act of 1934, which governs the FCC. Walden is chairman of the Subcommittee on Communications and Technology under the Committee on Energy and Commerce and his involvement improves the chances the bill will either be passed or incorporated into the larger rewrite of the Telecom Act.

Changes in the Telecommunications Act won’t necessarily affect Light- Squared, but changes over in the Senate might. Sen. Chuck Grassley (R-Iowa), who was tapped to head the Judiciary Committee when Republicans took control of the chamber. He was one of 33 senators who sent a letter to the FCC asking it to rescind a conditional waiver granted to LightSquared early in 2011. He also conducted a high profile investigation into whether the White House was playing favorites with the firm’s proposal.

That is not to say that New Light- Squared will be approaching Washington unarmed. LightSquared spent $1.14 million on lobbying last year and $160,000 during the first quarter of 2015, according to lobbying disclosure forms.

New LightSquared’s incoming board also has a couple of serious players. The chairman, according to court records, will be Ivan Seidenberg, a former chairman and CEO of Verizon Communications Inc. Joining him will be Reed Hunt, who was FCC chairman from 1993 to 1997.

Congress and the FCC are not, however, where the major action is at this point. The real sparring has been taking place at a series of workshops and standards meetings.

ABC Skirmishes
The Adjacent-Band Compatibly Assessment, now being conducted by the DoT’s John A. Volpe National Transportation Systems Center in Cambridge, Massachusetts, aims to head off future interference issues by letting companies know in advance the power limits by which they need to abide in order to avoid interfering with GPS.

DoT will first determine masks, that is, a set of power limits by frequency, for the bands near the GPS L1 signal. In thesecond phase it will do the same for frequencies near new GPS signals such as L5 and those near bands being used by other satellite navigation constellations.

“Step 1 is basically protecting all the existing receivers that are used for civilian applications and then Step 2 is looking into the future at what receivers might evolve into,” said Chris Hegarty, co-chairman of RTCA Special Committee 159 which deals with GPS issues, in a 2013 interview.

RTCA Inc. is a nonprofit association that coordinates volunteers developing consensus-based technical standards for the Federal Aviation Administration (FAA). It became involved when aviation officials asked for expert advice on six technical questions for the study. The FAA is working on the portion of the assessment that deals with certified aviation receivers; Volpe will handle the rest.

The group, with the exception of LightSquared, reached consensus in March. Given the time constraints and unlikelihood of agreement, LightSquared was invited to submit a minority report with its views to the FAA along with the committee’s results. LightSquared’s report suggested a significant number of technical changes to the overall study as well as the FAA portion, including not using a six-decibel safety margin in all cases.

“The 6 dB safety margin should be applied only to use cases where the aircraft is in motion,” the company wrote, “and reliant on GPS for navigation or other critical flight functions.”

A summary of the consensus view said the concern about using a six-decibel margin for ground operations had been raised with FAA, which responded that using six decibels was “applicable due to flight operations safety and regularity considerations” — that is, not just safe operations but also efficient operations. Regularity is a concept embedded in the International Civil Aviation Organization Safety Oversight Manual.

The six-decibel disagreement, explained Farrar, is about the size of the safety margin.

“One of LightSquared’s arguments has always been that no one will notice anything much,” said Farrar. “They [aviation officials] can say, ‘OK. This is what we need to guarantee everything will be okay,’ and LightSquared will say, ‘Well, that is overly conservative and nobody is going to notice if we go a bit over that.’”

LightSquared also wrote that it agreed with the FAA’s approach of using “change in position error as its key measurement to certify compliance with its GPS standards, underscoring the fundamental role of this measure in determining whether harmful interference is present.”

Volpe, it noted, chose to use change in the noise floor as its key performance indicator (KPI) for the task of defining the adjacent band mask.

“This is a critical distinction,” Light- Squared said in its dissent. “A position error analysis appropriately focuses on whether any kind of interference would actually result in a GPS receiver reporting a material change in position perceptible to the pilot or avionics system receiving the data (as defined in the FAA/ RTCA standards), and thus whether the interference actually qualifies as harmful interference. A 1 dB increase in the noise floor, on the other hand, has never been shown to correlate with any kind of predictable impact on the data reported by GPS receivers.”

The FAA, however, does not use actual position error by itself as its key metric, Hegarty recently told Inside GNSS.

“In the presence of interference at the mask levels, all the performance requirements must be met for the receiver,” he explained. “Position accuracy is just one of many performance requirements that the equipment needs to satisfactorily achieve in the presence of the specified maximum levels of interference.”

Chasing GPS Receiver Standards
LightSquared also recommended that the FAA’s approach should become the model for the rest of the assessment study — suggesting that, as for certified aviation receivers, receiver standards would be a good thing.

“FAA’s analysis,” wrote LightSquared, “and thus (RTCA’s Working Group-6’s) input to the FAA, assumes standards for GPS receivers that RTCA and the FAA had previously set for the function of certified GPS receivers — forward-looking minimum performance standards, as expressed in required receiver masks, that all GPS devices certified for use in aviation applications must meet.”

“While not specifically open for discussion in WG-6’s work,” the company added couple of pages later, “it is nevertheless important as a part of the overall DoT Study process to emphasize that RTCA’s recommendations, and FAA’s eventual analysis, are based upon standards for GPS receivers that RTCA and the FAA had previously set for the function of certified GPS receivers.”

“By contrast,” LightSquared continued, “the Volpe Center’s current plan is to establish a mask retroactively, and solely to limit power in bands of commercial spectrum adjacent to GPS based on the poorest performing GPS devices. The Volpe Center is not intending to establish (or recommend to the regulators of commercial spectrum) a set of minimum, forward-looking performance standards for the GPS devices it is studying. Rather, it plans to develop its mask based on a backwards-looking approach that seeks to protect all receivers, regardless of relevance or criticality of use case.

“Thus, while the FAA/RTCA approach can reasonably be shown to have the effect of encouraging receivers to be more resilient and safer (particularly as such standards are updated over time), the Volpe Center’s approach inevitably drives to protecting the worst performing devices and does not encourage development of more resilient receivers.”

It would not be the first time that receiver standards have been suggested.

In 2012 then-FCC Chairman Julius Genachowski, who was widely seen as an advocate for LightSquared, tasked the FCC’s Technological Advisory Council with studying the role of receivers and providing “recommendations on avoiding obstacles posed by receiver performance to making spectrum available for new services,” according to a post by the National Association for Amateur Radio.

The GPS community fiercely opposed standards at the time as a hindrance to innovation and a threat to the future of the millions of GPS receivers already installed and in operation. Now, the idea of receiver standards may have lost energy within the FCC. Tom Wheeler, Genachowski’s replacement as FCC chairman, took a decidedly different tack when addressing a 2014 workshop on protecting GPS.

“Today is about federal and nonfederal leaders coming together to discuss successful industry-driven collaborations and GPS receiver performance. These are not abstract issues,” Wheeler said. “But let me also be specific about what today is not. It is not about FCC-mandated receiver standards. Rather it is about the best way to protect GPS operations in the context of evolving technology and adjacent spectrum activities.”

Redrawing the Battle Lines
Both LightSquared and members the GPS community have stressed their willingness to work toward solutions, although their respective positions to not appear to have gotten much closer. If anything the two sides seem to be girding for battle in ever more distant corners.

Even so, the respective positions of the company and GPS advocates do not appear to have gotten much closer. If anything the two sides seem to be farther apart than ever.

LightSquared, for example, said in a March workshop held by the ABC Assessment team that agreement was not essential. When it comes to decisions on activities in commercial sectors of the spectrum the “FCC has exclusive jurisdiction,” said Geoff Stearn, vice-president for spectrum development at LightSquared.

The “only purpose of any such study,” he said of the ABC Assessment, “is to inform FCC regulatory action.” In fact, the company asserted that “the established process is for agencies to ask the FCC to conduct such studies.”

That assertion set off a furious exchange between the Light- Squared representatives including Stearn, and Don Jansky, a former associate administrator for the NTIA and president of Jansky-Barmat Telecom. Their exchange, in part:

Jansky: The FCC is not the final arbiter of the emissions. There is joint responsibility with NTIA. These agencies and NTIA do not report to FCC. They have a separate regulatory regime. And they have joint responsibility, particularly for the GPS band. So, they are not the final arbiter of what is the regulatory aspect regarding emissions. You should understand that. I don’t think you understand how spectrum management works in the United States.

LightSquared: I don’t want to continue the debate. I understand that that’s the NTIA’s view. The Communications Act invests the responsibility to regulate commercial enterprises, which is LightSquared, with the Federal Communications Commission.

Jansky: That may be for LightSquared, but it isn’t for the United States.

LightSquared: We’re talking about rules that would affect LightSquared.

Jansky: We’re talking about rules that would impact GPS. The next scene in the drama will be played out on a Washington stage. The Adjacent-Band Compatibly Assessment team will hold another workshop there on June 19.

LIGHTSQUARED LAWSUITS LINGER
During the same period that LightSquared was working to emerge from bankruptcy, it and its primary backer, Harbinger Capital Partners, between them sued the federal government, a number of GPS firms and associations as well as Dish Network chairman Charlie Ergen.

Though the case against Ergen and most of the allegations against the business community were dismissed, the lawsuit against the government is still active — well, if you use the word “active” loosely. The only action since the lawsuit was initiated in July 2014 has been seven requests for extensions.

More activity has taken place at the U.S. District Court. The remaining counts against the GPS business community are for negligent misrepresentation and constructive fraud and stem from LightSquared’s accusation that the GPS companies knew about, but failed to disclose in pre-2010 negotiations with LightSquared, their knowledge of and concerns about the “out-of-band reception” of GPS receivers that gathered GNSS spread spectrum signals in the MSS band as well as the GNSS L1 band.

The remaining counts leave the parties — Deere & Company, Garmin International, Inc., Trimble Navigation Limited, and the U.S. GPS Industry Council (USGIC) — open to discovery, Reuters noted in its reporting, which means that LightSquared could “probe the GPS companies’ books and records.” Any ability to obtain business or technical data is potentially significant. LightSquared, for example, has asserted that the ongoing Adjacent-Band Compatibility Assessment should only test top-selling devices.

Discovery, however, is a blade that cuts both ways, pointed out wireless spectrum expert Tim Farrar. If the case continues, LightSquared could find its secrets revealed as well.

The post Same Issues, Fierce Debate as LightSquared Bankruptcy Ends; GPS Spectrum Battle Reappears appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

More than 3,000 delegates converged on China’s ancient capital of Xi’an last week to infuse the sixth China Satellite Navigation Conference (CSNC) with an energy reflecting the nation’s robust GNSS program.

Organized by the Academic Exchange Center of the China Satellite Navigation Office (CSNO) under the slogan “Opening-up Connectivity Win-win” and vigorously supported by a dozen governmental and industry organizations, the event showcased the progress of predominately Chinese researchers, industry, and public officials in advancing the nation’s BeiDou Satellite System (BDS) and GNSS in general.

Located in north central Sha’anxi province, Xi’an (Western Peace, then known as Chang’an or Perpetual Peace) served as the imperial capital of Qin Shi Huang, who unified ancient China in 220 B.C., and nine succeeding dynasties. The city is also the site of the China Academy of Science National Time Service Center (NTSC), which monitors the offset between BeiDou system time and that of other GNSS systems, as well providing traceability of BeiDou system time to Coordinated Universal Time (UTC). (Xi’an is also the hometown of China’s current President Xi Jinping, who visited the city with India’s Prime Minister Narendra Modi during the week of the conference.)

Technical sessions offered a panoply of GNSS research and test results, some of which revealed that GNSS applications in China still lag behind those in the United States and Europe, but it’s a gap that won’t take years to close. Indeed, some presentations reflected ambitious accomplishments, such as the use of GPS and GLONASS to track the Chang’e 5-T1 flight launched last October that conducted atmospheric re-entry tests on the design service and return modules for China’s unmanned lunar exploration program.

Another intriguing paper explored applications of GNSS in construction and measurement of high-rise buildings, including monitoring (with  GPS and accelerometers) of the modernistic China Central Television Headquarters in Beijing (known locally as the “hot pants” building) that examined issues regarding its structural integrity. Sihao Zhao, from Tsinghua University, presented results of simulations of using BeiDou and other GNSS signals for attitude determination of China’s manned space station now in the planning phase.

A large exhibition accompanying the conference featured more than 120 exhibitors, including large aerospace companies, national and regional governmental agencies and institutes that support the BeiDou program, as well as many established and emerging GNSS manufacturers. Comments from exhibitors as well as conference sessions on regulatory policies, standards, trade laws, and patents and intellectual property reflected the interest of both the national administration and many manufacturers to begin marketing their products outside China.

Although organized as an international event (and featuring simultaneous English translation in almost all sessions), CSNC 2015 still drew a primarily Chinese audience, including many young engineers. Required use of Chinese bank–issued credit cards for online registration and an English-language website lacking in some details probably constrained overseas attendance. But foreigners who reached the conference venue were well rewarded for their efforts.

Presentations and conversations at the conference also reflected progress in bilateral talks between China and the United States, and China and the European Union, despite continuing trade and political issues that concern their respective governments at a higher level. Another round of talks between BeiDou and U.S. officials will take place in Washington, D.C., in June.

The post CSNC 2015 Raises BeiDou, GNSS Profile in China appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Judging from the variety of questions being asked at a recent EGNOS flight demonstration, the European Geostationary Navigation Overlay Service (EGNOS) remains something of a mystery for many of Europe’s leading aviation writers.

EGNOS, you say? What’s EGNOS?

Judging from the variety of questions being asked at a recent EGNOS flight demonstration, the European Geostationary Navigation Overlay Service (EGNOS) remains something of a mystery for many of Europe’s leading aviation writers.

EGNOS, you say? What’s EGNOS?

The May 12 event, masterminded by the European GNSS Agency (GSA), was meant to bring up to speed a lot of people who should have been on top of the program a long time ago, specifically members of the specialized European aviation press. Airbus Industries announced today (May 18, 2015) that the new Airbus A350 airliner, currently entering service, comes fitted with EGNOS as standard.

The all-day event at the Blagnac Airport in Toulouse, France, featured presentations by high-flying EGNOS officials as well as demonstrations of EGNOS-based navigation onboard a high-flying ATR 600. The views of France’s famed “Pink City” from up there were dazzling.

Of course, EGNOS is Europe’s satellite-based augmentation system (SBAS), the equivalent of the U.S. Wide Area Augmentation System (WAAS), which transmits corrections to GPS signals as well as issuing integrity alerts when the system should not be used. It’s been certified for safety-of-life applications such as airport approaches and landings since 2011, but few people seem to have noticed.

In the meantime, around 150 airports have adopted EGNOS-based landing procedures.

Some of the initial questions posed by the press felt rather elementary. No one seemed to know just where to start. But if there’s one thing journalists are good at, it’s getting up to speed; so, it wasn’t long before the hard questions were being asked. . . .

Such as why, after four full years, there are only 150 airports with EGNOS landing procedures? What’s the hold up? What’s stopping the airports from adopting EGNOS, if, as the GSA says, the benefits are so clear and overwhelming?

Not that 150 is nothing, it is certainly something, considering the hoops that have to be jumped through to get an aviation procedure published. But still, only 150? Compared to the 1,730 U.S. airports and more than 3,547 approach procedures using WAAS every day in the United States?

The tables were turned when the GSA asked a journalist a question: “So, what do you think about this event?” said the agency’s Donna Reay to U.S.-based Woodrow Bellamy III of Avionics Magazine. “No offense,” he responded, “but you guys are a little late.”

The experts say the problem is money.

Money, Money, Money
It costs £20,000 to £30,000 (US$30-45,000) to get an EGNOS landing procedure published in the United Kingdom, says Martin Robinson of the International Council of Aircraft Owner and Pilot Associations (IAOPA). The GSA’s Gian Gherardo Calini put the cost at a very similar €50 thousand across Europe, although, he added, that can vary wildly from country to country and from case to case.

Says the GSA’s GNSS exploitation program manager Jean-Marc Pieplu, “There is a gray area, between us getting the system up and delivering the signal, and then having all the airports equipped, procedures written, and using the system.”

Whatever the public and private benefits, whatever the positive business plan being espoused by the GSA shows, airports and operators and even pilots want proof first. They all understand that there is a significant initial financial outlay to be made.

In the United States, the FAA took things in hand when upper echelons decided it was time to get WAAS implemented at airports across North America. The funding was there, in the States, but then Europe has no FAA.

Again, Martin Robinson: “In the U.S. you had a deliberate industrial policy at the highest level. Aviation was recognized as an important industry.”

General aviation is worth $130 billion in the United States, he says, which is big business. The equivalent figure in the European Union (EU) is about $30 billion, still significant. “There’s got to be a business plan,” Robinson said.

Richard Farnworth of EUROCONTROL, the continent’s air safety organization, described the European scenario with its many actors, each one looking at the others and waiting to see who will blink (i.e., open their wallets) first.

“The U.S. benefits from [having] one FAA, one government, and a single policy. In the EU we have 29 of each,” Robinson said. “We need to join up, and the Member States need to be a little more willing to support the EU in introducing new technologies.”

And then there’s Europe’s inherently non-risk-taking industrial mindset. I may believe the investment will pay off, and you may believe the investment will pay off, and the GSA certainly seems to believe it, but the airports and the operators are the ones who have to believe it, and apparently some of them still find it hard to believe.

Don’t Despair
The GSA probably understands the problem better than most. That’s why it put €6 million on the table last year and is putting another €6 million on the table this year, this month, for co-financing creation of additional EGNOS landing approach procedures.

Pieplu says that last year’s call was oversubscribed, and he and his colleagues expect another big response this year. If nothing else, that means a fair number of landing procedures are in the pipeline.

Robinson says the work the GSA is doing is very important, encouraging local aerodromes to take on EGNOS landing procedures, which will ultimately create a better spread of locations for precision-guided approaches. Adopting the stance of another famous aviation visionary, he said, “No citizen should be more than one hour’s drive from an airport. Think about what that would do for local businesses.”

In case you’re wondering, the GSA offer is for co-financing of procedure writing, matching funds; so the question remains, is the other side ready to meet the GSA halfway? After the event in Toulouse, the agency hopes a few more people will understand why it makes sense.

As far as delivering the system goes, the EU is definitely sticking to its guns, with a major upgrade from EGNOS V2 to EGNOS V3 now in the works.

“Version three will feature two new capabilities,” says Jean-Marc Pieplu, “dual frequency – L1 and L5, and dual-constellation monitoring, both GPS and Galileo.”

“Today we are continuing with the preliminary design review. After that, by the end of 2016, we will launch the procurement phase,” Pieplu added. “Then comes the development phase, and we should have all V3 capabilities deployed and qualified for 2020-2023.”

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