One subject, more than any other, preoccupied attendees of this year’s European Space Conference. In a stirring welcome address, High-Representative/Vice-President of the European Commission Josep Borrell Fontelles told the assembly, “One year ago, we were just on the eve of war. Now, we are in the middle of a war, so the security of Europe in space is a very timely subject.” Our regular readers know that subject has been very timely for a while.

“Last year,” Borrell continued, “we stressed the increased level of threats in the space domain, and we now believe we need a change of paradigm. Space will become a kind of battlefield, of competition and confrontation. Satellite imagery and communications have proved to be game changers for the Ukrainian armed forces and the civilian population.”

Borrell recalled the cyber-attack on Viasat on the night of the Russian invasion that knocked out communications for several days, affecting neighboring countries as well as Ukraine. “This has revealed our own vulnerabilities,” Borrell said, “affecting our own member states. These are critical infrastructures that we need. If they fail, our economies, our entire lives, will be disrupted. In 2021, Russia tested a kinetic anti-satellite weapon. It was an irresponsible act that signaled to anyone that Russia is prepared to put anyone’s satellites at risk.”

Timo Pesonen, director-general, DG DEFIS, European Commission, also recalled the days just before the start of the war. “A year ago, if somebody in this room had asked who thought Putin was going to attack the capital of Ukraine in one month’s time, I’m not so sure how many of us would have raised our hands. Of course, there was intelligence information around that he was gathering troops at the border, but I think we were all hoping for a miracle to happen.”

What They Thought About That

The 15th edition of the conference was a return to form, that is to say a return to format. Two years ago, In 2021, the conference was mostly a ‘virtual’ online affair, due to the crisis we need not name. Then, in 2022, it went back to being held in person, but many habitual attendees stayed home, choosing to keep their distance until the coast was well and truly clear. By all accounts, that edition was a rather forlorn affair.

This year’s event felt like old times. The big plenary room was full to bursting, everyone was breathing deeply and the lunch hall, lobbies, corridors and side rooms were abuzz. And, as they will, fresh from the affray in the big room, people exchanged views candidly once outside. Having heard a number of high-profile presenters expressing their surprise and dismay at the events of February 2022, more than one corridor commentator expressed their own surprise at the surprise. “Didn’t Russia invade Georgia a few years ago?” said one, off the record. “Didn’t they just annex Crimea?”

Another well-known personality at the conference whispered, confidentially, “We were hearing every day from the intelligence experts. Of course we could see what was about to happen. There was no surprise.” Still another participant cited the regime in question’s repeated past “criminal” actions, saying the surprise was perhaps more about the absolute scale of the thing.

In 2017, Inside GNSS published an article titled, “EU and Russia: Lost in Space?” that questioned the advisability of maintaining a dependent relationship with Russia in the wake of that country’s aggressive actions against a familiar target. According to the article:

In April, the United States officially pulled the plug on almost all space cooperation with Russia as a result of the latter nation’s intervention in Ukraine.

According to a Space News report, cited in the 2017 article, Europe did not follow suit:

At the height of the bloodletting in eastern Ukraine last June, ESA [European Space Agency] Director-General Jean-Jaques Dordain said, “The European Space Agency has seen no signs that its relations with Russia will be curtailed as a result of the confrontation between Russia and the West concerning Russia’s actions in Ukraine.”

Even though:

Speaking in Brussels, one unnamed European official said, “The situation in Ukraine is very tense indeed, with many obvious consequences on the relationship between Russia and Europe.”

The Launcher Crisis

So much for warning signs. Europe now has a very present situation to contend with. In an era of ever-accumulating crises, we may add another: ‘The European Launcher Crisis.’ Borrell said, “This war was a wake-up call. We are becoming much more aware of the dependencies on foreign suppliers. For example, when the Russian Soyuz teams suddenly left the spaceport of Kourou, they put in danger our launch capabilities.”

“WE BUILT GALILEO IN RESPONSE TO AN EXISTING NAVIGATION NETWORK THAT WE ALL KNOW VERY WELL. TODAY, GALILEO IS PROVIDING THE MOST ACCURATE SIGNAL FOR NAVIGATION, FOR POSITIONING, AND THIS IS SOMETHING WHERE EUROPE CAN BE VERY PROUD.”

Josef Aschbacher, high-representative/vice-president, director general, ESA

But war isn’t the only problem. Josef Aschbacher, Director General of ESA, in his presentation of 2022 highlights, acknowledged other setbacks: “We had the successful launch of our MTG satellite on the 13th of December [onboard Ariane 5], but also just before Christmas, on the 20th of December, we had the failure of our Vega-C launcher, after we had had a successful inaugural flight in July, earlier in the year.”

According to reports, the Vega-C’s Zefiro 40 second stage deviated from its intended trajectory following a loss of pressure, resulting in reentry over the Atlantic, less than 1,000 km from its launch site. Two Airbus Defence and Space dual-use Pléiades satellites were lost in the misfire. Zefiro is a family of solid-fuel rocket motors developed by Avio.

“And this puts Europe in a very critical situation on launchers,” Aschbacher said, “due to the situation of our delays on Ariane 6.” Also in 2022, ESA again delayed the first flight of Europe’s Ariane 6 launcher, this time to late 2023.

“With this, and with the halt of the Soyuz launches from Kourou and the Vega-C failure, Europe is in a very serious situation. Guaranteed access to space is a top priority for Europe, for ESA, for all of us, because if we cannot guarantee access to space, we will seriously shut down launching of infrastructure on which we depend.” That includes completed Galileo satellites currently sitting on the ground, ready for launch.

Aschbacher said it is crucial to get Ariane 6 and a safe and functioning Vega-C onto the launch pad as quickly as possible, “but we also need to invest in the future, to engage a new group of launchers, micro-launchers, mini-launchers. And later we will need reusable launchers, after Ariane 6 and Vega-C. This is a weakness of Europe today.” Stéphane Israël, CEO of Arianespace, agreed: “In the long term, we will need a heavy, reusable launcher. For the European flagship programs, Galileo and so on, you will need a big launcher, no doubt.”

For now, the waiting goes on. A spokesperson for ESA told Inside GNSS the agency is actively seeking a solution for launching grounded Galileo satellites, which can include non-European (and non-Soyuz) launchers.

Self-Reliance Versus Dependency

An unintentionally provocative take: For all its ingenuity and scientific excellence, and in spite of Jules Verne, Europe has lagged behind as a source of inspiration in space. At the height of the Cold War, the U.S. and the Soviet Union drew the hearts and minds of the world toward the stars, on the tails of their military-fueled space race. Today, the Chinese government has managed to link its space program to the country’s immense sense of pride and their belief in its extraordinary destiny. Europe, on the other hand, for all its technical achievements and steady reliability, has remained a rather polite, very competent but otherwise low-key partner seeker. Not, one should add, without success.

Aschbacher said, “We built Galileo in response to an existing navigation network that we all know very well. Today, Galileo is providing the most accurate signal for navigation, for positioning, and this is something where Europe can be very proud.”

Miguel Romay, general manager navigation systems, GMV said, “When I started in navigation more than 30 years ago, Europe was completely out of the game. We had GPS, GLONASS and nothing from Europe. We started to move toward satellite navigation, dreaming about having something similar to what the Americans had.”

Must Europe always turn to others for inspiration? In this time of geopolitical turmoil and economic uncertainty, who will serve as Europe’s model? And on whom will it depend for help?

”EIGHT YEARS AGO, I FLEW TO SPACE ON A RUSSIAN VEHICLE, THE SOYUZ. A YEAR AGO, I FLEW ON A U.S. VEHICLE, NOT EVEN A GOVERNMENT VEHICLE BUT A VEHICLE PROVIDED BY A PRIVATE COMPANY AS A SERVICE. YOU HAVE THAT EXPERIENCE AND YOU START TO SCRATCH YOUR HEAD, AND THINK, ‘WELL, THIS IS GREAT. I LIKE INTERNATIONAL COOPERATION, BUT WHAT ABOUT FLYING IN A EUROPEAN VEHICLE?’”

Samantha Cristoforetti, ESA Astronaut

“We cannot implement the U.S. model,” Israël said. “The U.S. spends five times more on space than Europe. We can take some lessons, but the copycat strategy will not work. And this should not be about Europeans competing against each other. This is the U.S. and China competing against Europe. It’s time to organize the industrial base in order to compete. We see how it goes in the U.S., with the Inflation Reduction Act, how they have changed overnight the competitiveness of their companies against us. Let’s not be naive, let’s take the bull by the horns and make it happen.”

André-Hubert Roussel, president of Eurospace, said, “We don’t benefit from the measures that have been put in place by some of our competing nations, specifically for the U.S. industry. We are facing nearly 10% inflation in Europe. It’s going to cost our space industry 500-750 million euros in extra costs this year. We have to tackle this with our partners, starting with ESA and the EC [European Commission]. We need venture capitalists, and we need to make sure we have a level playing field with the U.S.”

Aschbacher said, “Today, Europe is not capable of launching its own astronauts, with its own capabilities, into space, because we are flying with our good friends and strong partners of NASA, the Americans. In the past few years, we were flying with Russia, but if you look 10 years into the future, I think Europe should seriously consider having its own capability. This is much bigger than space. It is geopolitical, it is societal, it is about the unity of Europe. We are not fast enough and we are not bold enough.”

Speaking of astronauts, ESA Astronaut Samantha Cristoforetti said, “Eight years ago, I flew to space on a Russian vehicle, the Soyuz. A year ago, I flew on a U.S. vehicle, not even a government vehicle but a vehicle provided by a private company as a service. You have that experience and you start to scratch your head, and think, ‘Well, this is great. I like international cooperation, but what about flying in a European vehicle?’ At this point, the question is what’s wrong with us? Why do we not have that ambition?”

”GALILEO HAS BEEN DESIGNED TO BE VERY ROBUST.
THE SPECTRUM OF SERVICES THAT ARE BEING DEPLOYED, STARTING FROM THE BASIC SERVICE, ADDING AUTHENTICATION, THE HIGH-ACCURACY SERVICE, THE PRS [PUBLIC REGULATED SERVICE], EMERGENCY SERVICES THAT WILL BE DEPLOYED IN THE FUTURE, SAFETY-OF-LIFE, WHICH IS PROVIDED BY EGNOS, ALL THIS NEEDS TO BE ASSURED.”

Javier Benedicto, director of navigation, ESA

“We live at the end of an era of happy globalization,” said Thomas Dermine, Belgium’s State Secretary for Economic Recovery and Strategic Investments, in charge of Science Policy. “We see a rise in geopolitical tension, we see commercial tension, we see a rise in protectionism. If you look at the American Inflation Reduction Act, it impacts all sectors. It is going to impact the space industry. A few years ago, we were seeing all kinds of cooperation with other parts of the world. We see today that our cooperation with Russia is completely ended. In China we see rising tension, and even with the Americans. We need to rely more on our own capabilities, because you don’t know what the future will be.”

Then Came PNT

Amid all the soul-searching, conference attendees suddenly found themselves faced with a series of presentations on what it all means for the positioning, navigation and timing troops. “It was already there before the war,” Pesonen said, “but the war has certainly underlined it. Resilience is a key word for us.”

”WE ARE OFFERING SERVICES THROUGH DIFFERENT SIGNALS, AT DIFFERENT FREQUENCIES, AND THIS IS PRETTY GOOD AGAINST INTERFERENCE. THEN WE HAVE OUR AUTHENTICATION SERVICE, WHICH IS COMING UP THIS YEAR, AND THERE WE WILL BE PRETTY GOOD AGAINST SPOOFING. WE CAN SPEAK ABOUT THE SECOND-GENERATION SATELLITES, WHICH ARE GOING TO PROVIDE MORE ROBUST SIGNALS.”

Paul Flamant, Head of Unit, Satellite Navigation, DG DEFIS, European Commission

Paul Flamant, Head of Unit, Satellite Navigation, DG DEFIS, European Commission, assessed the state of resiliency of Europe’s GNSS systems: “We are offering services through different signals, at different frequencies, and this is pretty good against interference. Then we have our authentication service, which is coming up this year, and there we will be pretty good against spoofing. We can speak about the second-generation satellites, which are going to provide more robust signals.

“But also,” he said, “it’s very important in terms of satellite navigation resilience that we keep our good agreements with those brilliant people that are on the other side of the Atlantic. We have agreements with the Americans, and it is very important that we continue this collaboration.”

Flamant also cited the EU’s European Radio Navigation Plan. “I invite people to read it,” he said. “In it, we call on people to come up with new resilience solutions, and we’ve been trying to see what really needs to be done, in terms of navigation but also in terms of timing. There were the workshops organized recently at the Joint Research Center at ISPRA, where we could see what other timing and navigation systems exist.”

“From the point of view of operations,” EUSPA Executive Director Rodrigo da Costa said, “we have two control centers, we have two security monitoring centers, so there’s a lot of resilience in there, but we also exercise that resilience. We carry out simulations, a number of simulated situations, working with our external action service, where we simulate techniques and responses. This is incredibly important in order to ensure the preparedness of our operational teams.”

A New Architecture

ESA Director of Navigation Javier Benedicto said, “Satellite navigation really has a very strategic dimension for all of us. It has become a commodity with respect to our daily life. It contributes to economy, but also to implementing government policy. This fundamental nature creates a notion of dependency. We depend every minute on it, and therefore there is an expectation on the part of users. These are systems that have to work all the time.

“This in turn creates responsibility, for the people who conceive the system,” Benedicto said. “Galileo has been designed to be very robust. The spectrum of services that are being deployed, starting from the basic service, adding authentication, the High-Accuracy Service, the PRS [public regulated service], emergency services that will be deployed in the future, safety-of-life, which is provided by EGNOS, all this needs to be assured.”

Benedicto outlined the new LEO PNT program adopted at the most recent ESA ministerial conference. “This program is the largest in the world of its nature,” he said. “It brings a new dimension, not based anymore on geostationary or MEO satellites but also on LEO satellites. The trick of the business is to interconnect all that from the user perspective. The user does not know or care if the signal is coming from a LEO or a MEO or GEO satellite.

“We will also see an evolution affecting the EGNOS service, today based on geostationary signal broadcast, but in the future also to be broadcast most probably from MEO and LEO satellites. And we see a future in deploying navigation signals in new frequency bands, not only in the L band but also in lower bands and higher bands. All this leads to a multi-layered PNT architecture.”

A Constellation for Digital Resilience

IRIS2 (Infrastructure for resilience, interconnectivity and security by satellite) is the EU’s newest space-based infrastructure, being mounted in record time and offering enhanced communication capacities to governmental users and businesses, and delivering high-speed internet broadband in connectivity dead zones. Initial services are scheduled for launch as early as 2024, with full operational capability by 2027.

Benedicto said, “IRIS2, Galileo and EGNOS all have to be connected, because, at the end of the day, we want to reach the smartphone, we want to reach the airplane cockpit, the dashboard of the autonomous vehicle, and this requires a combination of sensors and techniques for both communication and navigation. This will require the use of optical technologies, quantum communication, quantum encryption, and with all of this, I am sure that Europe will remain at the forefront of resilient PNT.”

On that very inspiring note, we leave the 15th European Space Conference, with apologies to all who were not cited. We will meet again, and so say not adieu, but au revoir. Until next time…

The post Brussels View: European Space Conference 2023 Takes a Hard Look appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Today, we are assisting to a proliferation of high accuracy applications on smartphones, thanks to the availability of dual frequency measurements along with the capability to process GNSS raw measurements on Android devices. Here the authors address a new level of sub-meter positioning accuracy, before unimaginable without professional grade equipment, and now accessible to everyone on smartphones, to people on all budgets.

denotes the receiver code noise at measurement L1 (in meter), and fL1 and fL2 denote the center-frequency of first and second frequency respectively (in Hz).

The post Galileo Hits the Spot: Testing GNSS Dual Frequency with Smartphones appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Due to the proliferation of personal privacy devices and other jamming sources, it is imperative for safety-critical GNSS users such as airports and marine ports to be situationally aware of local GNSS interference. This article proposes and validates an enhanced method for geolocating GNSS interference sources so that jammers and spoofers can be found and disabled.

Wideband jammers or interference, intentional or not, are most effective at jamming nearby GNSS users as it is frequency agnostic and causes sustained loss of GNSS signal by nearby GNSS users (see Borio et alia, Additional Resources, posted in the online version of this article). Examples of intentional wideband jammers uses modulation schemes such as FM chirp (sometimes also known as swept Continuous Wave) and Additive White Gaussian Noise (AWGN). In comparison, narrowband jammers such as Continuous Wave (CW) are relatively frequency selective and can cause intermittent GNSS operation. Hence, it is imperative to have the ability to identify the presence of a wideband GNSS jammer and geolocate it as they pose a greater danger to GNSS users.

It is well known that phased arrays can be used passively to determine the AOA of a Radiofrequency (RF) emitting signal source. Typical passive GNSS interference sensing uses a network of phased arrays to infer two or more Angles of Arrival (AOA). Conventional AOA estimation using phased arrays assumes narrowband signals that satisfy the time bandwidth product:

where

is defined as the maximum distance (in meters) between station pairs and B is defined as the signal bandwidth is Hertz. To accommodate wideband signals, most AOA algorithms (see multiple papers, Additional Resources) partition the signal into multiple narrowband channels for AOA processing. Thus, such methods are attempting to geolocate wideband sources despite the source being wideband. Examples of algorithms that are able to deduce the AOA from phased arrays are MUSIC (Schmidt) and MVDR (Rieken and Fuhrmann).

The AOA measurements from two or more stations can then be used to triangulate the location of the interference source. By deploying multiple stations of phased arrays that are geographically dispersed, AOA estimates retrieved from each station can be used to accurately geolocate a source using techniques such as least-squares that are based on the intersection of AOA lines of position (Dempster).

To exploit fully the wideband characteristics of the source signal, a cross-correlation method needs to be employed. The cross correlation of received signals between two distant stations will produce a distinct peak at a cross-correlation delay with the Time Difference of Arrival (TDOA) of the corresponding source. Hence, TDOA is another sensor station’s observation that relates to the geographical location of the wideband source signal. If there are three or more stations, the combination of two or more detected TDOA measurements can be used to geo-localize the jammer position.

Recently, new methods consider a modified Maximum Likelihood (ML) approach for AOA geo-localization dubbed direct positioning (see both Cheong and Dempster, and Tzafri and Weiss). Indeed, cross-correlation and AOA characteristics can also be simultaneously exploited for geolocation in direct positioning. However, this occurs at the expense of orders of magnitude greater in computational cost and is beyond our scope.

Until recently, source geo-localization algorithms have only considered either AOA measurements or TDOA measurements as independent systems of equations for geo-localization. By and large, conventional algorithms have not appropriately considered the fusion of heteroskedastic (i.e. unequal variances) AOA and TDOA measurements and have not fairly characterised their advantages against pre-existing methods.

Modern techniques have attempted to integrate AOA with TDOA using computationally expensive constrained optimization techniques (Bishop et alia). Another method attempts to integrate AOA with TDOA by assuming at least one Time of Arrival (TOA) is available (Li and Weihua). While this may be afforded by CDMA cellular networks that are active systems, passive sensing systems like those considered here are unable to obtain TOA. Some papers considered unweighted algorithms, not considering that AOA and TDOA measurement standard deviations vary from station to station, which is highly unrealistic (see again Li and Weihua). The most recent progress has been made by Yin et alia where AOA and TDOA are combined for geo-localization in closed form but that method is unable to cope with incomplete information; hence one missing AOA measurement from station i, for example, will result in the complete loss of all TDOA measurements involving station i. This lack of robustness ultimately affects the geo-localization accuracy.

Closely based on Cheong et alia, Additional Resources, this paper presents empirical results for combining AOA and TDOA measurement to consistently obtain superior geolocation accuracy. We will also show that the empirical results fit its theoretical error models.

AOA Geolocalization

We consider a vector of AOA measurements from L stations

modelled as a Gaussian distribution

as described by Yu, Additional Resources. Here, the true AOA is denoted as

and the AOA measurement is denoted as

which has a normal distribution with variance
.l specifies the station index. Its error covariance matrix

where the off-diagonal elements are zero and the diagonal elements

corresponds to the l-th station’s AOA error variance in units of rad2. It is important to convert all angle measurements and covariances into units of rad for all AOA related processing. In this paper, the origin of AOA corresponds to the East direction and increasing AOA is in the anti-clockwise direction.

To accommodate the heteroskedastic nature of AOA measurements, a Gauss-Newton approach to geolocate the source can be taken. The first step for deriving a Gauss-Newton solution is to identify the Jacobian matrix Ja which is the gradient to the linear approximation to the geolocation process at every new iteration. Detailed relationships of the AOA measurements with respect to the jammer’s coordinates (Xu, Yu) can be found in the paper by Dempster. This procedure is then iterated until convergence as summarised in Pseudocode 1. Starting from a source coordinate initialized using a conventional technique, Pseudocode 1 iteratively evaluates the linearized approximation using the Gauss-Newton approach and updates the Jacobian Ja to yield the final jammer coordinates

Notice that in the absence of any AOA measurement

does not affect the overall operation of pseudocode 1. The only prerequisite of pseudocode 1 to achieve convergence is that Ja cannot be rank deficient (i.e. need to have full rank). This can be ensured by geographically spacing out the stations from each other as best as possible throughout the surveillance area. Based on the Cramer Rao Lower Bound (CRLB), the error covariance of the AOA-only geo-localisation estimate

is expressed as

as reported in the work of Xu and Doʇançay.

TDOA Geolocalization

Let us define the source’s TDOA of station i from station j as τij. Then if we construct the observed TDOA from all stations with reference to station j=1 as

the TDOA observation model used is a multivariate Gaussian distribution. [6].

The mean and the covariance component of the multivariate Gaussian distribution can be further defined as:

Note that

is the true TDOA of the i-th station from station 1, and

is the variance related to the signal arriving at the i-th station.

Given that we are considering a wideband GNSS jammer, we can use cross-correlation signal processing techniques to measure the TDOA arriving between several stations. The TDOA measurement is related to the source coordinates (Xu, Yu) by

Note that ||.|| is the Euclidean distance of the vector. From its partial derivatives, we can construct the Jacobian matrix for the TDOA measurement vector as Jt as described in the paper by Kaune et alia. A Gauss Newton algorithm can be derived using its gradient Jt for a TDOA-only source geo-localisation algorithm. The implementation of this iterative process can be summarised in Pseudocode 2.

Pseudocode 2 considers TDOAs from a star network topology. If a fully connected network is considered, the algorithm can still cater for all the TDOAs, but its effect on accuracy is negligible. However, in the case where there are certain missing TDOA measurements, this algorithm can still easily adapt to that. Based on the reasoning for the case of AOA-only geo-localisation, the accompanying error covariance of the position solution based on the CRLB is.

Proposed AOA/TDOA Integrated Geolocalization

This section presents the core contribution of this paper, that is to be able to geolocate by fusing both AOA and TDOA measurements. Given the AOA-only and TDOA-only geolocalization estimates (i.e. and ) which can be computed from Pseudocode 1 and Pseudocode 2 and their respective errors covariances Σt and Σa, their solutions can be found by using the Weighted Least Squares (WLS) as follows,

Where the integrated error covariance matrix and the transformation matrices are respectively,

The appropriately weighted AOA/TDOA loose integration requires perfect knowledge of the AOA-based and TDOA-based position error covariance matrices. The calculation of AOA-only and TDOA-only covariance matrices ΣuT and ΣuA in turn requires sufficiently accurate coordinates of the source and station. While the station coordinates are known, the source coordinates are generally unknown. In practice, this solution is implemented as an iterative process because the best guess of the source coordinates is at the end of each iteration. Thus, in such an iterative procedure, a guesstimate position from either the AOA or TDOA estimation process can be used in the first iteration to initialize both the TDOA and AOA position covariance matrices. After the AOA/TDOA integration has been performed, subsequent iteration uses the updated position estimates to re-calculate Σu,T and Σu,A. This is repeated until convergence. Indeed, we have experimentally verified that in some, but not all cases when (Xa, Ya) or (Xt, Yt) is sufficiently accurate, the number of iterations for AOA/TDOA integration K can be as small as one. The maximum number of iterations is considered sufficient when the Euclidean difference between the coordinate estimates

of iteration K and K-1 is within a desired tolerance. This convergence principle applies also to Pseudocode 1 and 2. This iterative process is detailed in Pseudocode 3.

While this proposed architecture of integration can be thought of as “loose”, it has the advantage of accommodating existing TDOA-only and/or AOA-only geo-localization implementations that are already in place. It also has very low computational cost and low complexity as it does not require complicated forms of numerical optimization techniques, nor does it require evaluation of non-linear functions, as tighter integration methods do.

To obtain the CRLB for the integrated solution, we first need to derive the Fisher Information Matrix (FIM) as

The CRLB for the joint AOA/TDOA geolocation is defined as the inverse of the FIM:

Numerical Results

For this section, we intend to visualize the geometry-dependant accuracy of AOA-only, TDOA-only and our proposed AOA/TDOA integrated solution over a realistic configuration of station baselines. We consider three stations in an ENU coordinate frame: (0, 0), (-2, -740) and (-383, -2). These coordinates correspond to the field trial configuration in the next section. In Figure 2, the superimposed geographical lines corresponding to a range of constant TDOA (iso-TDOA lines) spaced at 200 meters for a scenario with three stations is shown. An iso-TDOA line indicates the direction with zero gradient, hence the tangent to the iso-TDOA line is the direction with the greatest descent or greatest ascent.

For a fair comparison, it is important to assume appropriate measurement standard deviations that are realistic for both the AOA and TDOA systems in a convex region formed by three stations. The convex region corresponds to red grid points in Figure 3. For this section, we used AOA standard deviation of 0.3° and a time of arrival standard deviation of 1 meter as they are the worst case observed in our field test. For the following results, the effect of coverage is considered. Thus, the source location needs to be within 2 kilometers of a station for its AOA to be measurable and at least two AOA measurements are needed for AOA-only geo-localization, whereas TDOA can only be measured when the source location is in coverage of two stations and three stations are needed for TDOA-only geolocalization.

Figure 3 is produced by sorting the source’s position indices according to the CRLB of joint AOA/TDOA geo-localization and superimposing its corresponding AOA-only CRLB and TDOA-only CRLB. The superiority of joint AOA/TDOA estimation is obvious. The CRLB of either AOA-only or TDOA-only geolocalization within the convex region is within 2.0±0.5m and 5.0±3.0m, respectively as seen in Figure 3. In the same convex region, the CRLB of the joint AOA/TDOA geolocalization maintained within 1.0±0.3m. Hence, the AOA/TDOA geolocalization yields two major benefits. First, the CRLB at any point within the convex region will experience an overall reduction, in this case, a median of at least 50% improvement. Secondly, the stability of the positioning error within this region will have far smaller fluctuation; up to tenfold improvement in stability in this example.

The horizontal CRLB of all three methods are shown in a colormap in Figure 4. Commensurate with Figure 3, these figures show clear improvements delivered by the joint AOA/TDOA geolocalization.

The improvement, i.e. CRLB reduction, experienced when switching from an AOA-only geolocalization to a joint AOA/TDOA geolocalization is shown in Figure 5. The percentage improvement peaks near the edges of the convex region where the AOA measured from a pair of stations have a difference of {0,π} radians. The joint AOA/TDOA geolocalization performs significantly better than AOA especially at these boundary points due the increased diversity in geometry when both AOA and TDOA measurements are considered for geolocation. From the TDOA-only geolocalization perspective, the joint AOA/TDOA geolocalization similarly yields an average of approximately 35% CRLB reduction within the convex region. This can be seen from Figure 5.

Field Test Result

We verify our proposed method against data collected from a specialised open area calibrated test range. The range is a remote site in southern Australia that permits actively monitored and controlled transmissions of weak signal GNSS jamming & spoofing for experimental purposes. The 1 km2 range consists of three passive sensor arrays (each with a circular concentric array of eight element antennas, see Figure 7) as stations. As the sensor arrays operate in the GNSS band, it uses beam-steering to exploit the GNSS signal for calibration without being affected by the interference. This configuration is visualised in Figure 6. The positional inference from AOA measurements are not visualized here.

TThe stations are spread across three corners of the almost rectangular test range with local East-North coordinates (0.0, 0.0), (-1.9, -739.8) and (-383.1, -2.3). The station’s hardware was used to detect the occurrence of jammers and the computation of its AOA and TDOA measurements. It performs MUSIC processing of AOA measurements; whilst the TDOA measurements were computed via cross-correlation of baseband signals.

In this field test in early 2017, we deployed two wideband jammers as stationary sources at GNSS-surveyed coordinates (-114.0, -199.8) and (-375, -304.5) for source 1 and source 2, respectively. The antenna is mounted on the two vehicles as depicted at the right of Figure 8. The GNSS antenna used for ground truth is shielded from the source’s antenna and is positioned at the null of the jammer antenna’s radiation pattern. Its RF cable is also guided away from source’s antenna before entering the equipment in the vehicle. The source transmitter is a BladeRF x40 (with a bandwidth of 20MHz) controlled by an Intel NUC small form factor PC running Linux.

Using the setup in Figures 7, 8 and 9, we collected 261 epochs valid of AOA and TDOA measurements from all three stations. These are logged from the GRIFFIN hardware which performs MUSIC processing for AOA estimation and cross-correlation processing for TDOA estimation in real-time.

We estimate the TDOA and AOA statistics from the dataset itself. The standard deviation of AOA for source 1 at stations 1, 2 and 3 are 0.05°, 0.12° and 0.30°, respectively. For source 2, those are 0.10°, 0.13° and 0.16°, respectively. The TDOA standard deviations for source 1 are 0.875m and 0.860m for the measurements between station 1-2 and station 1-3, respectively. For source 2, the TDOA standard deviations are 0.845 meters and 0.988 meters for the respective station pairs. An example for the gaussian fit on these measurements are shown in Figure 10.

The computed coordinates of the AOA-only geolocalization, TDOA-only geolocalization and the proposed AOA/TDOA geolocalization is shown in Figure 11. The scatter plot for both sources show good agreement between the CRLB 3σ error ellipses and the empirical scatter points. Furthermore, the empirical scatter points for the proposed AOA/TDOA algorithm exhibited an equal degree of improvement as predicted by its theoretical CRLB error ellipse. More importantly, the geometry of the AOA (red) error ellipse and the TDOA (black) error ellipse is shown to complement each other to produce an improved accuracy for the joint AOA/TDOA (blue) error ellipse.

The theoretical and empirical error statistics for the East and North component are shown in Table 1 and Table 2 for source 1 and source 2. The AOA and AOA/TDOA error statistics are highly commensurate with bounds dictated by the theoretical CRLB.

The minor discrepancy between the empirically measured 3σ error and the theoretical 3σ CRLB can be attributed to minor differences between the modelled distribution of AOA and TDOA error and the actual AOA and TDOA error distribution (see Figure 10). Specifically, the empirical TDOA error statistics are unable to be correctly captured by a simple Gaussian model due to minor unmitigated timing variation and localised multipath effects.

From Table 1, we can compute the empirical Euclidean 3σ error for source 1 as 3.82 meters, 2.42 meters and 1.39 meters for AOA-only, TDOA-only and AOA/TDOA joint geolocalization. The corresponding errors for source 2 shown in Table 2 are 3.44 meters, 4.01 meters and 2.22 meters. RMSE reduction is empirically shown for the proposed AOA/TDOA method for source 1 to be at 63.6% from an AOA-only geolocalization and at 42.5% from a TDOA-only geolocalization. For source 2 the RMSE reduction is empirically shown to be ranging from 35.4% to 44.6%. These improvements are also commensurate with theoretical expectations as dictated by the CRLB.

While some of the features of the AOA/TDOA integration methods may enhance the positioning performance only in decimeters, they can be in the order of tens or hundreds of meters as the AOA and TDOA measurements increase in error variance due to weaker transmit signal strength or greater geographical inter-station separation.

Application

While the scale of the RMSE enhancements are small in our field trials, such improvements should not be underappreciated. To illustrate how our proposed method affect large scale deployment, we adopt the Kingsford Smith airport in Sydney, Australia as the scenario. The simulated stations are situated at (-1744, 2555), (1318, 1180) and (-32, -2262). As we only seek to understand the effects of geometrical variation, we base our TDOA and AOA measurements covariances to our field trial data to compute the horizontal CRLB in this simulated scenario. The results are shown in Figure 12. Notice the significantly enlarged contours of equi-RMSE for the joint AOA/TDOA method in comparison to the AOA-only or TDOA-only. The joint AOA/TDOA method also does not suffer from undesirable fluctuation in accuracy as seen in the AOA-only method.

In absolute terms, the CRLB predicted RMSE are large because we have considered three stations over an approximately 3km x 5km coverage area, which is an order of magnitude larger than the coverage of our GRIFFIN open area test range. Also, the contours indicate the maximum CRLB. Actual CRLB near the center of the convex region of three stations are substantially smaller.

Conclusion

We have proposed a new integrated AOA/TDOA geolocalization algorithm that can be used for passively sensing and geolocating a wideband GNSS jammer and characterized its theoretical error distribution via Cramer Rao Lower Bounds. Also, we theoretically analyzed the proposed integrated AOA/TDOA with realistic covariances in a hypothetical environment and found that this approach can deliver substantial reduction in root mean squared error (RMSE) over conventional AOA-only or TDOA-only geolocalization, when averaged across the entire test range. Additionally, we also show in a real-world experiment employing the GRIFFIN network of time-synchronized phased arrays that our proposed approach can deliver up to 63.6% and 44.6% of RMSE reduction when compared against AOA-only and TDOA-only geolocalization.

In absolute terms, our approach has delivered up to 2.43m reduction 3σ error in a real-world experiment, bringing the resultant horizontal 3σ error down to 1.39m. In our tests, the size of the approved GRIFFIN interference test range has limited our ability to test the case for stations spread across longer baselines. By way of extrapolation, our proposed methods can potentially deliver hundreds of meters of improvement in accuracy as visualised in a simulated deployment at an airport.

Acknowledgments

This work was jointly funded by the Australian Research Council (ARC) and GPSat Systems Industry through Linkage Project LP140100252. The field tests were supported by GPSat Systems Australia Pty Ltd.

Manufacturer

The stations used GRIFFIN prototype engineering hardware manufactured by GPSat Systems Australia Pty Ltd in early 2017 as supporting contribution for its ARC Linkage project with UNSW. The GRIFFIN hardware has since undergone substantial engineering changes. GRIFFIN is a series of hardware and software suite for detecting and geolocating jammers and spoofers in one or more GNSS spectrums. On 15 August 2019, the Australian Ministry of Defence announced that GRIFFIN is currently being taken into production via its Defence Innovation Hub program.

Authors

The post Detecting and Geolocating Jammers and Spoofers Using Integrated AOA and TDOA Measurements appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Studying and testing new and possible future GNSS signals and navigation messages require a signal generator that is flexible and fully modifiable. To overcome the need for implementing a signal generator from scratch, we present a way to modify an existing GNSS software receiver (SR) into a software transceiver (ST). The ST reuses the SR modules and the infrastructure for the signal generation. The modification approach is based on exploiting the vector-tracking feature of the software receiver. Due to the replacement of the position in the vector-tracking loop, it is possible to manipulate the numerical controlled oscillator (NCO) and thereby force the code and carrier generator to generate a signal replica which fits the induced position. Multiplying the replica with the desired symbol value and the desired amplitude yields an entire line-of-sight (LOS) signal. The replica signals of all satellites in tracking match the predefined user trajectory. Saving the added replica signals produces a signal stream at intermediate frequency (IF) which can then be converted to an analog radio frequency (RF) signal. The ST can track, generate, or regenerate tracked signals. The concept and an implementation approach are presented with an assessment of the induced errors arising from this approach. The tracking performance of the generated signals is compared to the theoretical limits.

Research in the field of GNSS signal performance under spoofing, jamming, and multipath comes with the need for reproducing their channels, signals, and scenarios as well as possible. Performance of current signals can be analyzed quite easily as it is possible to use the genuine transmitted signals or use state of the art commercial off-the-shelf (COTS) signal generators to recreate the setup. However, this becomes much more difficult if new signals, channel structures, or navigation messages are under test. Some commercial signal generators have the possibility of implementing new signals and using their own navigation message, but only to a very limited and restricted extent.

To overcome these restrictions, it is necessary to implement one’s own signal generator (see M. Petovello and C. Curran in Additional Resources) to have all the possibilities in creation and testing signals in all desired scenarios. However, implementing a sophisticated signal generator from scratch is a huge, difficult, and time-consuming task.

With an ST it is possible to reuse the sophisticated and optimized infrastructure of an SR for the signal generator. We exploit the fact that each SR must create an estimated replica of code and carrier for the correlation. The key element in our approach is using the software receiver vector-tracking architecture to create the desired LOS parameters for updating the NCO and therefore the code and carrier replica generation (T. Pany and B. Eissfeller).

Feeding the LOS module with the position, velocity, and time (PVT) of a pre-defined receiver trajectory gives an easy opportunity to manipulate the vector-tracking loop to generate replicas as needed to recreate signals that represent the receiver movements. In addition, the desired symbols and amplitudes must be provided to the tracking loops. Multiplying the replica signal with amplitude and symbol yields a new channel IF signal batch of samples. Adding up all channels produces an IF signal stream for a total or even multiple constellations. If required, additive white Gaussian noise (AWGN) can be added before the IF signal batch is written into the output stream. Ionospheric and tropospheric influences are simulated as the line-of-sight parameters are calculated using ionospheric and tropospheric models. The ST is able to track, generate, or regenerate tracked signals. A similar approach for regenerating tracked signals to upgrade existing receivers was presented by Humphreys et alia (Additional Resources).

We first present and explain the SR and the vector-tracking architecture. Thereafter, the modifications are described to convert a software receiver into a software transceiver, using the vector-tracking approach. The implementation as well as error sources are addressed, using the MuSNAT SR as foundation. We assess error sources and finally we compare the tracking and positioning performance of the ST generated signals with the theoretical limits and give examples for the current usage of the ST.

Software Receiver, Vector Tracking Architecture

Vector tracking is an old topic in the GNSS community and was first proposed in 1980 by Copps et alia. Since then, many papers, articles, and books have been published, describing and studying the topic in its full complexity and broadness with all its advantages and drawbacks (see Additional Resources). The references given are far from complete and can only be a starting point for interested readers.

To understand the necessary modifications for transforming a vector tracking-based SR into a signal generator (software transceiver, ST), a brief description of the conventional independent channel tracking architecture and the used vector tracking architecture follows.

All SRs have two main modules defining the processing workflow between the IF sample stream as receiver input and the receiver PVT information as receiver output: the signal processing unit with the tracking loops as its core, and the navigation processor. The signal processing unit, especially the tracking loops, process sample batches of the IF sample stream in sequential order and extract the pseudorange and pseudo range rate p for all tracked satellite signals. These parameters are passed to the navigation processor which then determines the receiver’s PVT.

In a conventional SR, separate and independent tracking loops track each satellite signal as a standalone signal. The signal processing is schematically shown in Figure 1. Each tracking loop consists of a correlator, integrator, discriminator, loop filter, NCO, as well as a code and carrier generator. The code and carrier generator create a replica of the received satellite signal. The replica generation parameters are controlled by the NCO. An early (E), prompt (P), and late (L) version of the generated replica signal is then correlated with the IF sample batch. The values for the integrated correlation signals are dumped and handed over to the discriminator. The discriminator evaluates the E, P, and L values for in-phase (I) and quadrature-phase (Q) and estimates the code time delay error as well as the carrier frequency error and the carrier phase error of the replica signal. Over the loop filter these estimated error values are used to update, i.e., speed up or slow down the NCO, so that the replica signal follows the satellite signal. A detailed description of the signal processing is provided in the Additional Resources. With the replica values, a good estimation of the satellite signal parameters (code delay, carrier Doppler, and carrier phase) can be determined. The satellite signal parameters are used to calculate the pseudorange and pseudorange-rate (Doppler) which are needed for the PVT determination in the navigation module. The update rate of the tracking loops is on the order of 50 to 1,000 hertz, whereas the navigation processor works with a common update rate on the order of 1 to 10 hertz. The loop filter with its bandwidth is used to smooth the high rate values for the NCO update. Therefore, the current smoothed output values of the loop filter are used by the navigation processor at the measurement epoch.

In a conventional SR all tracking loops work independently as mentioned above. This creates the drawback that the tracking loop can easily lose the satellite signal during short periods of signal blockage or signal fading. Even if the signal outage is short, the receiver must start with a reacquisition of the lost signal, which is time- and processing power-consuming. The basic idea of vector tracking is to support the single tracking loop with redundant information from the other tracking loops, so the single tracking loop is able to bridge periods of a weak signal or signal blockage. All satellite signal parameters are defined with respect to the PVT of receiver and satellite. Therefore, the PVT solution of the navigation processor represents the compressed information of the redundant tracking-loop outputs. The vector tracking task is to feedback this redundant information to each single tracking loop, or more precisely, to feedback the best estimation of it. This task can be realized in various forms, some more and some less complex. The more sophisticated approaches usually use some kind of extended Kalman filter (EKF) for PVT estimation (if this filter also includes inertial measurements, it is called a deep GNSS/IMU integration). The implementation used here is sketched in Figure 2, where the PVT algorithm is intentionally left unspecified. An additional module to determine the LOS parameters for all tracked satellites is needed. The satellite position is known from the ephemeris in the navigation message. The navigation module will update the LOS parameter with a rate of 10 hertz (100 ms). The operational update rate of the tracking loop, however, is around 1,000 hertz (1 ms). To overcome this lack of sampling points, a quadratic extrapolation of the LOS parameters is performed, assuming a constant acceleration until the next LOS update. The equations for the code extrapolation look like:

with  

This extrapolation is obviously wrong in the long term but, as we will show later, for the short time span of 0.1 second the approximation works quite well for systems with moderate dynamics. These extrapolated LOS parameters are used to update the NCO in a hard reset fashion. This means that the replica signal changes during the 0.1 second in a smooth fashion, i.e., determined by Equations (1)–(3). After the update of the LOS parameters, however, a jump occurs in the range and range-rate domain of the replica signal parameters. The error of the extrapolation and the jumps of the replica signal are compensated in the pseudorange calculation, as the discriminator determines

For the carrier phase the Doppler values are integrated and reset at the reference epochs to match computed LOS phase values.

Due to the fact that the loop filter is no longer needed to determine the smoothed update values for the NCO, a new decimation filter is needed to provide smoothed pseudorange estimation values to the navigation processor. This can be a Kalman filter as in J.-H. Won et alia, but we use a polynomial fit method, where a vector of discriminator values is fitted with a polynomial function to determine the signal parameters at the measurement epoch (see again, T. Pany and B. Eissfeller).

Software Transceiver

An SR needs to mimic and create the satellite signals as well as possible to be able to track the signal hidden under the noise floor. So, all SRs are already signal generators. The only difference is that an SR reproduces an existing signal and is not creating a new one. To transform the SR vector tracking architecture into an actual signal generator, two main modifications (shown in Figure 3) must be applied.

First, one needs access to the created replica signal which consists of

transceiver implementation following the concept described above. The implementation is based on the Multi-Sensor Navigation Analyzing Tool (MuSNAT) (Additional Resources).

Signal Generation

The MuSNAT SR processes IF sample batches of ~30 ms. This IF sample batch is divided into IF sample frames with a length of ~ 0.8 ms. The sample frames are processed in sequential order. In each processing step, the sample frame is distributed to all Master Channels. Each Master Channel processes one satellite signal and is composed of one or two tracking channels depending on whether the satellite signal has one (data) or two (data and pilot) components. All these channels process the sample frames successively. Due to this sequential workflow, we are able to allocate memory in the receiver with the same size as the IF sample batch and handover memory pointers to the tracking loops. In this way, all tracking loops can add their locally created satellite signal to this memory segment. When the IF sample batch processing is finished, the generated IF sample batch is processed in total. In this final processing step, AWGN can be added and output conversions can be performed. In the conversion step, the generated IF stream output format is set. Internally, the replica signals are stored as double-precision float values, to ensure minimal quantization errors in the signal addition process. For the saving process, the double values can be converted to the desired output format. In this work, either a real-valued 8bit or an I-Q-8bit conversion is used.

For the signal amplitude, we use predefined C/N0 values for each satellite in tracking. The amplitude parameter for each satellite s is defined as a relative amplitude. The reference satellite signal strength is 45 dB-Hz with an assigned reference amplitude of 1, weaker signals are assigned a corresponding smaller amplitude. The calculations were done with the equation

The standard deviation of the AWGN was calculated with:

where Aref=1 and C/N0ref = 45 dB-Hz, as the AWGN is adapted to the reference signal. The equations above were derived from M. Petovello and A. Joseph

where powerequals the sampling rate. SNR and BW denote the signal-to-noise ratio and the bandwidth. The amplitude value can also be used to implement a land mobile satellite (LMS) channel model (A. Lehner and A. Steingass) to simulate more realistic signal propagation conditions including multipath fading and blocking.

The last missing part for the signal generation represents the navigation symbol D. The navigation message has to be pre-produced and is stored in a receiver internal data base. D(t-τ) is selected for each sample frame according to the satellite transmitting time.

Here the SR usage provides the benefit that the data flow is already organized in a way that single frames refer to only one data symbol value.

User PVT Trajectory Feedback

For the user trajectory input we use a text file of the format shown in this example:

where traj is the trajectory time, Px, Py, and Pz represent the user position in the WGS 84 system in meters and represent the user velocity in the WGS 84 system in m/s. The trajectory points (2 hertz) need to be interpolated to the PVT update time steps (10 hertz). To do so, a fourth-degree spline interpolation approach was chosen. The fourth-degree spline interpolation is defined with the following three equations:

The interpolations in the x, y, and z dimension are done independently from each other. The PVT solution also includes values for clock error and clock drift as well as accuracy estimations (mean radial spherical error, MRSE) for position and velocity. Clock error and clock drift are set to zero in a first step, position and velocity accuracy is set to 0.001 m and 0.001 m/s. These parameters can be defined in the trajectory input file and are set as needed. The interpolated PVT solution is handed over to the LOS module.

In this module, the LOS parameters are determined for all satellites in view and above a defined elevation angle. Therefore, the satellite position at the transmitting time is determined. Thereafter, the distance between satellite and receiver is calculated. In addition to Earth rotation and relativistic effects, ionospheric and tropospheric models are applied. The derivatives of the pseudorange are approximated by linear considerations.

In the next step, the LOS parameters are transferred to the tracking loops. Here a second extrapolation step is needed to adapt the sampling rate from the 10 hertz of the navigation process to the 1,000 hertz of the tracking loops. The quadratic extrapolation is already described above in the vector tracking section, see Equations (1)–(3). In the normal vector-tracking process, this approximation is acceptable because the error is compensated by the discriminator value. However, this approximation becomes an issue for the signal generation. The replica is created, following the extrapolated values, and a replica jump occurs at the edge of the last extrapolated point to the first point of the new extrapolation. These jumps could make it more difficult to track the generated signal, especially for the phase tracking. An assessment regarding this error follows in the next section.

Accuracy Assessment

Error model

The interpolation and extrapolation process is visualized in Figure 4 with a sinusoidal movement. Figure 4 is just for illustration purposes of the errors induced by the interpolation and extrapolation processes. To estimate the errors induced by the extrapolation process we use a very simple but representative user/satellite movement model. As the maximum values for

occur when the user movement and the satellite movement are in the same plane, we reduce the three-dimensional problem to the two-dimensional satellite orbit plane. The user movement is assumed to be only in this plane, on a spherical Earth with radius. The satellite orbit is also assumed to be circular with the radius. The 2-D movement model is sketched in Figure 5. In an Earth-centered-Earth-fixed (ECEF) frame, the receiver and satellite position can be calculated with

Error Simulation

With Equations (23)–(25) we can calculate the range, range-rate, and range-rate-rate of a static or dynamic user. In Figure 6 the results for a static user and a user with a very long periodical movement are presented. The long periodical movement with a frequency of 0.001 hertz and a maximum acceleration of 1 m/s2 was chosen as it nicely illustrates the behavior over one total satellite cycle. The receiver movements are visible one-to-one in the range and its derivatives when the satellite has an elevation angle close to zero degrees. In contrast, the receiver movements vanish in the range values for an elevation angle of 90°. This was expected and is of no surprise but shows the correctness of our assumptions. In the next step we calculate the range error ep with Equation (27) and evaluate the maximum error for a specific satellite elevation for an extrapolation time dt=0.1 second. For the static receiver case there are only very small values and small variations for (see Figure 6), therefore, the extrapolation process is uncritical and is below 20 nm for all elevation angles. For a high dynamic receiver case we chose a maximum acceleration of and a movement frequency of 1 hertz. In this case the receiver changes its acceleration from +10 m/s2 to –10 m/s2 in 0.5 seconds. This is similar to an accelerating sports car whose driver later slams on the brakes. The maximum error over the satellite elevation is shown in Figure 7 (High Rx Dynamic). For the medium dynamic receiver case, we chose the values amax=2 m/s2 and f=0.5 hertz, Figure 7 (Medium Rx Dynamic). A low dynamic receiver case is approximated with a maximum acceleration of (acceleration of a train) and a frequency of 0.5 hertz, Figure 7 (Low Rx Dynamic). Comparing the three different cases it is evident that the change in the maximum acceleration (medium versus low) has a similar impact on the total range error as a change in the dynamic frequency (high versus medium). As the approximation assumes a constant acceleration in the extrapolation time dt, it is clear that a fast change in the acceleration creates a significant influence on the range error. The second dominant parameter on the range error is of course the extrapolation time dt. In Table 2 the errors for different simulation parameters are listed for a fixed satellite elevation of 15° and a maximum acceleration of 10 m/s2. If we assume that a carrier phase error (jump) of 1% is tolerable, we end with the requirement <1.9 mm. Therefore, the extrapolation error is only acceptable for low and static receiver dynamics, using an extrapolation time of 0.1 second. If the update rate is increased to 100 hertz, even high receiver dynamics create only small extrapolation errors <<0.1 mm, again, see Table 2).

Results—Pseudorange Verification

To verify the correct implementation and the consistent LOS parameter creation, the values for pseudorange, pseudorange-rate, pseudorange-rate-rate, and the carrier phase were plotted and evaluated using debug information for the respective build ST software module. The pre-defined receiver trajectory resembles a circle with a diameter of ~400 meters and the receiver velocity equals ~60 km/h, which results in a period of ~75 seconds and a frequency of ~0.013 hertz. The trajectory is plotted in Figure 8. The generated satellite configuration consists of eight GPS satellites and seven Galileo satellites, the sky-plot with all generated satellites is shown in Figure 8. The extrapolated LOS parameters of the Galileo satellite with PRN 24, are plotted in Figure 9. In the pseudorange plot and the phase only the satellite motion is visible as the pseudorange is governed by the satellite movement. In pseudorange-rate and pseudorange-rate-rate, however, the velocity and acceleration changes caused by the user movements are clearly visible. In the zoomed plot a constant behavior of and a linear behavior of is observed between the quadratic extrapolation jumps. For the pseudorange, no jump can be detected. These results match perfectly with the above described accuracy assessment. The expected range error for satellite PRN 24 (elevation 24°) with the described receiver movement is only on the order of ~12 μm.

Tracking and Positioning Performance

To check the quality of the generated signal, we compare in a first step the tracking performance of the generated signal with the theoretical value. The used tracking parameters are displayed in Table 3. The tracking results of the ST generated Galileo OS signal of PRN 24 are presented in Figure 10. The measured value for SNR was 27.776 dB and C/N0 = 51.76dB-Hz. According to these parameters, the theoretical values can be calculated as described by Thomas Pany (T. Pany in Additional Resources). The theoretical standard deviation of the code discriminator is. Comparing the measured standard deviation of  small divergence of 2.1% can be observed and is similar to all tracked satellites. The results are very close to the theoretical minimum and proof a nearly perfect channel generation.

In a second step, the Single Point Position (SPP) of the tracking result was compared with the initially defined (“real”) user trajectory. Therefore, the seven Galileo satellites were used for the SPP solution. In Figure 11, the error δ of the SPP solution to the “real” trajectory is plotted in the local east-north-up coordinate frame. The mean position error and the standard deviation error of the SPP position in the east, north, and vertical directions are given in Table 4. The theoretical values are calculated with

where DOP is the Dilution Of Precision value in the corresponding direction and is the pseudorange standard deviation with a theoretical value of 0.090 m. A comparison of the values is given in Table 4. As indicated in Table 4, a gap exists between the theory and the measured noise values of 2–4 decimeters. The reason for the difference is not clarified yet and is under investigation, however, the very small mean position errors prove a bias-free position generation.

Areas of Application

The MuSNAT signal generator was already used to implement and verify Navigation Message Authentication (NMA) (see Additional Resources) foreseen for Galileo Open Service (OS) signals. In this activity, real Galileo satellite signals were recorded and processed with th

e MuSNAT to extract the symbol-stream (navigation message) and satellite ephemeris for each satellite in sight. In the second step, the spare bits in the Galileo E1B INAV navigation message are replaced with the OSNMA authentication bits. Thereafter, the symbol stream, the satellite ephemeris, and the desired C/N0 values for the tracked satellites were read into the GNSS-Transceiver to generate the IF sample-stream file. For analysis, the IF sample file was again processed by the MuSNAT-Transceiver to extract the bits and verify the authentication. The detailed analysis of the NMA is presented by Maier et alia. In the same work, Maier et alia used the MuSNAT signal generator to study the influence of secure code estimation and replay (SCER) attacks (again, see Additional Resources) on a software receiver. In contrast to the NMA test, these tests would not be possible with a COTS signal generator system, due to the need for modifying the generated signal on a deeper level.

Conclusion and Outlook

In this work, it was shown that the conversion of a software receiver into a software transceiver using the vector-tracking approach is feasible and quite easy to realize. The basic concept was presented with an accuracy assessment on the error sources. The implementation of the concept using the MuSNAT software receiver was explained. The quality of the generated signals was compared with the theoretical lower limits. It was shown that the satellite channel could be generated very accurately.

As shown in the accuracy assessment section, the extrapolation range error varies over a wide range, depending on the satellite elevation, the maximum acceleration, the acceleration change rate, and the extrapolation time. The range error is below 1 mm even for high receiver dynamics if an update rate of 100 hertz is chosen. For an update rate of 10 hertz only static and slow receiver movements can be generated with sufficient accuracy. As the current generator settings use an update rate of ~10 hertz, it is planned to increase the update rate and study the possibility of replacing the quadratic extrapolation step with an interpolation, very similar to the trajectory interpolation. To do so, not only the current LOS parameters but also the future LOS parameters need to be passed to the tracking loop. These updates will allow us to create continuous and consistent satellite signals for all possible receiver dynamics. Additionally, the implementation of LMS channel models is planned to increase the realism of the signal generation.

Acknowledgments

This work is funded by the German Federal Ministry for Economic Affairs and Energy on the basis of a decision by the German Bundestag. It is administrated by the German Aerospace Center in Bonn, Germany, (FKZ: 50 NA 1703)

Additional Resources

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(3) Galileo SIS ICD, “European GNSS (Galileo) Open Service Signal-
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(4) Humphreys, T., B. Jahshan, and L. Brent, “The GPS Assimilator: A Method for Upgrading Existing GPS User Equipment to Improve Accuracy, Robustness, and Resistance to Spoofing,” Proceedings of the 2010 ION Conference, Portland, 2010.

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(10) Pany, T. and E. Bernd, “Use of a Vector Delay Lock Loop Receiver for GNSS Signal Power Analysis in Bad Signal Conditions,” Proceedings of the 2006 IEEE/ION Position, Location, And Navigation Symposium (PLANS), 2006.

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(13) Petovello, M.G. and C. T. Curran, “18. Simulators and Test Equipment,” Springer Handbook of Global Navigation Satellite Systems, Springer, 2017.

(14) Petevello, M., M. Lashley, and D. M. Bevly, “What are Vector Tracking Loops, and What are their Benefits and Drawbacks?,” Inside GNSS, May/June, 2009.

(15) Petovello, M. and A. Joseph, “Measuring GNSS Signal Strength,” Inside GNSS, November/December, 2010.

(16) Psiaki, M. L. and T. E. Humphreys, “GNSS Spoofing and Detection,” Proceedings of the IEEE, Volume: 104, Issue: 6, pp. 1258-1270, 2016.

(17) Stöber, C., M. Anghileri, A. Sicramaz Ayaz, D. Dötterböck, I. Krämer, V. Kropp, D. Sanromà Güixens, J.-H. Won, B. Eissfeller, and T. Pany, “ipexSR: A Real-Time Multi-Frequency Software GNSS Receiver,” Proceedings of the IEEE 52nd International Symposium ELMAR, 2010

(18) Wesson, K. D., M. P. Rothlisberger, and T. E. Humphreys, “A Proposed Navigation Message Authentication Implementation for Civil GPS Anti-Spoofing,” Proceedings of the Radionavigation Laboratory Conference, 2011.

(19) Won, J.-H., D. Dominik, and E. Bernd, “Performance Comparison of Different Forms of Kalman Filter Approaches for a Vector-Based GNSS Signal Tracking Loop,” NAVIGATION, Volume: 57, Issue: 3, pp. 185-199, 2010.

(20) Won, J.-H. and T. Pany, eds., “14. Signal Processing,” Springer Handbook of Global Navigation Satellite Systems, Springer, 2017.

The post GNSS Transceiver: Simulation Accuracy Assessment of Using a Vector-Tracking Receiver as RF Constellation Simulator appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Every GNSS has experienced a failure. On January 26, 2016, an error in the GPS data upload system caused incorrect data to be transmitted from the satellites on the L1 band used by most commercial GPS receivers. The problem was resolved within six hours, although some users experienced problems for as much as twelve hours. The next day, the US Air Force (USAF) released a full statement explaining that the problem was caused by ground system software when one satellite was decommissioned.

Russia’s GLONASS and the Chinese BeiDou system have also experienced technical glitches, of greater or lesser severity. But what happened to Galileo in July 2019 was unprecedented. By the European Commission’s own account, the total system failure lasted from 10–17 July. During that time, according to the European GNSS Agency (GSA), “A team composed of GSA experts, industry, ESA and Commission, worked together 24/7 to address the incident.”

According to a European Commission spokesperson, “The technical issue was solely related to the ground infrastructure in a Galileo control centre, not to the Galileo satellites. The incident took place during an important upgrade of the control centres, when the standard redundancy between the centres was not available. The incident impacted the time and orbit determination function and prevented the correct generation of navigation messages.”

That it took all of a week to resolve the problem is surprising, but equally troubling was the lack of communication that accompanied the outage. The official Notice Advisories (NAGUs) posted on the European GNSS Service Centre website were slow to appear and provided little detail. Meanwhile, no voice was heard, no face was seen, no representative of the program was available for an official comment.

Eventually, some days into the event, the GSA did post a statement on its website, taking responsibility and even apologizing for the failure. But then it attempted to minimize its import by arguing that, after all, Galileo is still in its “initial services” phase and therefore should not be expected to work without interruption. Among many Galileo’s supporters, that message fell flat.

Users of smartphones may or may not have noticed the Galileo outage at all, but the design engineers, systems integrators, application entrepreneurs and service providers that had invested heavily in Galileo did certainly notice, and were severely discouraged.

Duty to Report

During that week when Galileo was offline, Inside GNSS received many messages and questions from its readers and correspondents. The messages were informative and important. They revealed concern, disappointment, even fear. Inside GNSS received these messages not because it represents the Galileo program in any way, but because, for many who were surprised by how the Galileo failure was playing out, Inside GNSS was apparently the only responsive ear to which their messages could be delivered.

We believe the comments of these correspondents may be useful to the Galileo program.

One GNSS industry executive wrote: “It’s inexcusable how long it took after the failure occurred until the user base was informed. Even worse the SIS/health flag showed no problems until the very end. I was surprised. A total constellation failure should not be happening in the modern world of testing and simulation. The reaction was slow and until this day has been very secretive and political. As there has been no official communication, all my theories [concerning the technical cause of the failure] are derived from Inside GNSS!”

In the absence of proactive and timely messaging from the Galileo program to service providers who market products in part based on their Galileo features, these providers were effectively left “holding the bag.”

A European commentator expressed concern while watching and waiting for information: “The handling of the situation was a failure in itself; the first NAGU published 14 hours after the event, no information about the cause and no indication about the time to recovery, followed by a second one with more or less the same approach.

“Galileo is strategic for Europe, it has managed to get close to completion, but the organization and its mindset need to change radically. They have not yet fully understood what providing a critical service means.”

Further Reaction

After the technical crash was resolved, the comments became more circumspect. A representative from the automobile industry told us, “The Galileo outage raises a key operational concern for vehicle manufacturers. While Galileo satellites were evidently healthy between July 11–18, it took the GSA control segment six days to identify the root cause and fix the outage. This clearly shows a weakness in ground operations that has implications to worldwide users. I would like to see a statement from the GSA about how they intend to safeguard against similar outages in the future.”

A North American GNSS engineer wrote, “I look forward to Galileo becoming fully operational—these are very useful signals! This was likely a good learning experience, but learning experiences are rarely pleasant. I’m looking forward to the constellation status update at ION GNSS+ in September.”

Finally, an academic involved in development of one of the major non-US, non-European GNSS programs told us, “In my opinion, in space engineering, due to a large number of links, brief failure is inevitable, such as satellite state abnormal in a short period of time. However, I wonder why Galileo system had been down for so long this time. I guess this failure has exposed more than technical problems, there should also be management problems.”

Answers?

As a result of the July episode, the Galileo program now has a major branding problem on its hands. It needs to explain what went wrong and how it will move forward.

The unprecedented technical failure and the protracted search for a fix to the underlying technical issue will be challenging to explain.The subsequent communication lapse exposed what is perhaps the program’s greatest weakness: inadequate governance arrangements.

The program rests on three pillars. One pillar is ESA, one of the premier organizations of any kind in the world. It has carried out countless space missions of the highest order, flawlessly. Another pillar is the GSA, comprising a group of carefully selected and world-leading experts in the field of GNSS, including contingency planners and rapid-response teams, capable of delivering at least near-military-level reliability.

Under the current Galileo governance structure, neither of these organizations is able to speak on its own initiative concerning the Galileo failure. This is why we have been unable to relay to you any words on the subject from any person within these two organizations. This is in turn because, under the Galileo governance structure, both entities are subservient to the third pillar, the European Commission. Under the circumstances surrounding the July 2019 incident, ESA and the GSA could only repeat the Commission’s own prepared words. Only the Commission could inform us. Unfortunately, the European Commission was probably the least suited of the three to respond to the need for direct, frank and open communication. And apparently it still is.

A few days ago we sent some questions to the relevant European Commission spokesperson, the only person officially allowed to communicate with the press on this matter. The description that we received of the technical root cause, cited above, months after the failure, remains vague.

More surprisingly, the Commission continues to use the “it’s-only-the-initial-phase” excuse to minimize the significance of the event. The official response reads, “The interruption of services was indeed unfortunate. But it is important to bear in mind that Galileo is, since December 2016, in its ‘Initial Services’ phase…It is not uncommon for a complex global navigation system in its ‘initial services’ phase, to experience temporary issues affecting the quality of the signal.”

What’s surprising about this response is that key Galileo operatives within the Commission are aware that the “initial-phase” explanation was roundly rejected by the GNSS community when it was first offered, months ago.

The spokesperson’s service continued to vaunt the program’s communications response, in the form of the NAGUs issued on the European GNSS Service Centre website. The GNSS community, by and large, found those NAGUs inadequate.

These official responses, crafted to deflect criticism while revealing little new information, do not seem to be in touch with the GNSS community. They do not sound like any of the familiar “voices of Galileo” who know us and whom we recognize and respect.

Who Speaks for Galileo?

Galileo is managed within the European Commission as part of the EC Directorate General (DG) for Internal Market, Industry, Entrepreneurship and SMEs, also known as DG GROW. As its name implies, this DG manages much more than just the EU’s space programs. It also oversees initiatives related to EU competitiveness, financing for small businesses, European chemicals regulation, CE marking, etc., and it contributes to a variety of other initiatives related to the European single market.

All the activities carried out by DG GROW are under the responsibility of the politically appointed EU Commissioner for Internal Market, Industry, Entrepreneurship and SMEs, and of that commissioner’s hand-picked cabinet. The Commissioner, who is not necessarily a GNSS expert, indeed who is not necessarily an expert in any of the fields covered within the DG that he/she oversees, serves for five years and then is replaced by a new Commissioner.

There are many well-known and trusted operatives within the Galileo program, both at the Commission and at the GSA, who are perfectly capable and who were, we know, willing and eager to address the system failure openly and in real time when their intervention could have made a difference.

But their hands were tied. The clearance they need to speak about anything as sensitive as the Galileo failure can come only from “cabinet level,” that is, from the team that immediately surrounds the responsible Commissioner. The Galileo program was ready to respond but it was held back by operatives who were possibly concerned more about politics than serving the Galileo user community.

One of the reasons details about the failure were and continue to be kept under wraps is that once the nature of the technical failure is divulged, the identities of the contracted partners responsible for the failure are likely to be deduced. That might prove embarrassing to those contracted partners. If, on the other hand, responsibility for the failure were to be incorrectly attributed to such a partner, they might be justified in seeking retribution for injuries sustained to their reputations. While these are probably real and valid concerns, they are also clearly political concerns.

Many now recognize that, under certain circumstances, such as those that occurred during the July 2019 failure, the Galileo program might be better served if the people who run the program were allowed to do their work without this kind of restriction.

Not as Bad as All that

The European Commission is a powerful tool for achieving real progress of a steady, dependable and valuable nature. It is a fundamental EU institution with crucial roles to play. Establishing overall policy goals, gathering political support, obtaining financing and applying that financing to the pursuit of world-class research and many crucial policy initiatives are only some examples.

However, due to its particular management arrangements, specifically as they concern the Galileo program, it was unable to respond effectively and quickly to the July 2019 crash.

That’s the bad news. Here’s the good news: Galileo will change. The changes need not be drastic. It might be enough for the European Commission to allow its own Galileo unit to operate with a little more autonomy under certain extenuating circumstances. Just let them do their jobs. We leave that to the Commission to decide.

Future Perspectives

“If nothing else,” someone suggested, “Galileo has been a fantastic experiment and will serve as a model of international cooperation for future generations.”

Unlike GPS, GLONASS and BeiDou, which are largely single-nation programs, Galileo has required the coming together of 28 very different countries. The variety of thought, culture and tradition among European countries makes that a challenge. It’s a challenge faced every day by the European Union, and with gusto.

“Whatever happens, Galileo will be held up as an example of how to work together, and in some cases how not to work together,” another source commented. “In that sense at least it already has enormous value. The new challenges facing today’s world are, after all, global and require a level of cooperation that has been uncommon up to this point. Just look at terrorism, the migration crisis and environmental protection, climate change. We have seen the difficulties that these challenges involve in terms of international cooperation.”

Is Galileo then just a case study? An example? If so then right now it needs to be an example of how to learn from one’s mistakes. It must now be an example of agility, flexibility, and perhaps even humility, in the face of failure.

The Galileo system was famously promoted from its inception as a non-military system. But other GNSS programs demonstrate that there are some things military organizations can do quite well. Indeed, there may be some tasks that require a more military mindset, such as the delivery of a critical day-to-day global utility.

Last January, EC Commissioner Bienkowska famously said, “We need to promote a new European approach. We need our own European vision, not look to others.” After a burst of applause from her European audience, she continued, “For example in the US, they have a national space council, attached directly to the President. Why couldn’t we have a European Space Council attached directly to the European Council or to its president?”

Bienkowska was right on both counts. Europe can move forward and succeed as Europe, learning from its mistakes, adapting and evolving. It can and should continue to look to its international partners, to seek examples abroad of how GNSS can work. In 1777, the young Marquis de Lafayette said to General George Washington, “It is not to teach but to learn that I come hither.”

We now understand that the Galileo program has invited certain key representatives of the GNSS industry, among others, to a “Galileo post mortem,” to take place on September 9. We expect that some listening and learning will occur at that event. We hope the proceedings will be made available in real time to the widest possible public. As yet, we know of no representatives of the press who have been invited.

This could become a great defining—or redefining—moment for Galileo, as it moves into a new phase. We all eagerly await Galileo’s next moves and next words.

The post Brussels View: Lessons to Be Learned From Galileo Signal Outage appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Geographical information plays a permanently increasing role in our society. More and more devices and applications use and process geographical information to serve all kinds of purposes. Smartphones, cars, e-bikes, scooters or foot shackles for law enforcement purposes collect and process geographical information on a permanent basis. Here, we take a close look at privacy issues and the data protection perspective, namely considering the European GDPR and experiences gained one year after its entry into effect.

An extensive range of today’s applications on all sorts of devices are based on geographical information and therefore geolocation data. Think of map apps or popular dating apps, social networks and messenger apps containing whereabout and geotagging functionalities. Geographical information is regularly collected from all of us, playing an important role in our daily lives. It is therefore critical to clarify the legal framework applicable to hardware, software, applications and services equipped with or based on data generated by location-sensitive sensors.This article describes the data protection perspective, particularly considering the European General Data Protection Regulation (GDPR) and experiences gained in the year since its entry into effect. Many of the devices and applications generating or using geographical information are intimately linked to a specific individual. Most people keep their smartphone and similar devices very close to themselves, from the breakfast table to their pocket or handbag, to the workplace, to the bedside table. Cars, e-bikes, or scooters accompany people on their daily commute and during business or recreational travel.

 

Geolocation makes it possible to obtain all types of information in real time and locate the user with pinpoint accuracy at any given point in time, from any device connected to the Internet. This allows manufacturers of devices and providers of geolocation-based services to gain a very intimate and accurate overview of user habits and patterns. They can build extensive profiles, and even to link such profile information to all kinds of other information. Such profiles may also include highly sensitive categories of data, such as information about visits to specialized physicians or hospitals, religious or cultural places, or political demonstrations. Profiles can easily be used to prepare and make decisions that significantly affect the individual in an unprecedented form and manner.

Such constant and extensive monitoring, analytics, use and dissemination of location data generates unpredictable risks, not only for individuals concerned, but to an equal extent for service providers facing potential attacks and data breaches, and in the sequence of events, possible sensitive punitive measures by supervisory authorities. Such risks increase exponentially due to rapid technical progress and largely unhindered commercial exploitation. Particular attention must be paid to risks connected with monitoring carried out secretly, without properly informing the individual concerned. Many users ignore or “forget” that location data processing or even location services are switched on or are performing as “background applications.” To ensure a legal framework to mitigate such risks and define ways for companies to use such data, the GDPR established a framework for processing personal data, including geolocation data.

GDPR Impact on Geolocation Information

The GDPR Regulation (EU) 2016/679 became applicable throughout the entire European Union on May 26, 2018. It has a major impact on data protection discussions worldwide. Originally conceived as an instrument for further harmonizing the different data protection standards of EU member states, the GDPR has such a broad scope of application that its influence extends far beyond EU borders. It may be applicable for companies in third countries, even if such companies do not have any establishment within the EU.

General Principles of the GDPR

The GDPR sets the legal framework for businesses located within the EU processing personal data, ensuring a high level of data protection. The GDPR’s basic principles stipulate that the processing of personal data must be lawful, fair and transparent, carried out with a strict purpose limitation, based on the principle of data minimization, and always ensuring appropriate security (Art. 5 (1)).

Foremost, the rights of individuals (called data subject — an identified or identifiable natural person whose personal data are processed) have been harmonized, renewed and extended. The right of access to one’s own personal data (Art. 15) does not only include the right to information on such data, but also the right to request an electronic copy. The right to deletion enforces the corresponding controller’s obligation to minimize data processing, once the purposes for processing are accomplished. The data subject also has a right to object to the processing of the data, which is to be complied with without limitations for direct marketing purposes (Art. 21).

To observe the data subject’s rights and provide a proper protection of personal data, suitable technical and organizational measures, not only on IT security, are to be adopted. Such measures must be updated regularly according to the current state of the art in IT technology.

Tackling the risks, mentioned earlier, of unintentional or even secret data collection, the principle of privacy by design and default was prioritized (Art. 25). This requires proof by the controller that no more personal data than necessary for each specific purpose are processed, and that personal data is not made accessible by default if not required. This is particularly relevant for geolocation data, which should only be collected when specifically required for the purposes requested by the data subjects.

The conditions for violations of the GDPR have been considerably strengthened, with fines up to 20 million EUR (approximately $22.21 million), or in the case of a company group, up to 4% of the total worldwide annual turnover of the preceding financial year (Art. 83 (3)). However, it should be noted that such high fines will only be imposed in cases of severe GDPR breaches.

Personal Data, GDPR Applicability Defined

The GDPR’s definition of personal data includes all information relating to an identified or identifiable living natural person. Personal data within the scope of the GDPR therefore includes device IDs, location data, browser types, IP addresses, etc.

While (online) identifiers (e.g. user ID, IP address, etc.) are not considered personal identifiable information, since they alone cannot be used to identify a person, the GDPR considers it sufficient that any entity may identify a person, irrespective of the fact that a link between the “de-identified” information and the identifying information may only be created in the most aggravated circumstances. Therefore, even if the controller itself does not own or does not have access to the identifying information, the data can still be considered personal data if any other entity may identify the person based on the information held. As an example, telecommunications companies and website operators can establish a clear link to the customer via the IP address of a person and therefore may establish a connection between IP address and username. Therefore, the IP address and other similar identifiers constitute personal data, according to the GDPR.

If data is pseudonymized–all identifiable characteristics are replaced by identifiers–such data can still fall under the term personal data as used in the GDPR, if such data can be used for the renewed identification of persons.

Anonymized data are neither personal identifiable information nor personal data. Such data must however be processed in a way so that they cannot be traced back to a natural person. This may for example be the case for financial data, statistical data for data used for research purposes.

Territorial Scope of the GDPR

The scope of the GDPR can also affect companies located outside the EU, including in the U.S. Companies fall into the territorial scope of the GDPR if personal data are processed either by “an establishment of a controller or a processor in the Union, regardless of whether the processing takes place in the Union or not” (Art. 3 (1)). Therefore, the GDPR is applicable if a foreign company has a branch in Europe which processes personal data. Even renting of office space or having an individual representative within the EU can constitute an “establishment.”

Even companies with no establishment in the EU may fall under the GDPR’s territorial scope, as all companies that offer goods or services in the EU or observe the behavior of EU citizens are subject to the GDPR (Art. 3 (2)). The definitions of goods and services are not to be interpreted restrictively: it is enough to obviously intend to offer services in one (or more) EU member states.

While the mere accessibility of a website in the EU is not enough, price labeling in local currency (e.g. EUR) or websites in local language (e.g. French or German) may indicate the intended provision of goods and services in the EU. Lastly but very importantly, any activity linked with behavioral monitoring of EU data subjects opens the GDPR’s applicability.

Thus, the GDPR applies in many cases where a company, due to its location, would not generally assume its applicability. As a direct consequence, the processing activities falling under the territorial scope of the GDPR have to comply with the GDPR and the respective entity has to designate in writing a representative in the EU (Art. 27 (1)).

Lawfulness of Processing Personal Data

The processing of personal data is only lawful if occurring on an explicit legal basis. Otherwise, such data processing is prohibited. A legal basis can either derive from the data subject’s free consent or from an explicit statutory permission.

In practice, the most relevant legal basis for the processing of personal data derives from the controller´s legitimate interests (Art. 6 (1) sentence 1 lit. f). The question as to the existence of legitimate interests must be answered by a balancing of interests: the legitimate interest of the controller on the one hand and the opposing interests of the data subject on the other. In principle, the definition of a legitimate interest covers any legal, factual or economic interest. This is the case, for example, where “there is a relevant and appropriate relationship between the data subject and the controller in situations such as where the data subject is a client or in the service of the controller” (Recital 47).

Further, a contractual relationship between the controller and the data subject (not: the data subject’s employer) constitutes a legal basis for all data processing which is necessary for the performance of such contractual relationship (Art. 6 (1) sentence 1 lit. b GDPR). The concept of “necessity” may not be interpreted too strictly; processing is already necessary for the performance of the contract if no less incisive, economically equally efficient means are available.

Furthermore, the controller can only rely on the consent given by the data subject. If, for example, the provider of a navigation software compiles profiles on the movement of its customers for personalized marketing activities, such processing will generally require consent. A consent to data processing must be given freely, unequivocally and with full knowledge of all background information about the data processing of the data subject´s personal data. Thus, full disclosure of the processing activities is key for obtaining valid consent.

Since the legal basis (or rather the purposes of processing) cannot be exchanged at one’s own discretion, it is important to identify and lay down the explicit purpose and the respective legal basis for every processing activity upfront. The processing activities must then be designed in such a way that they comply with the conditions set out in the respective provisions of the GDPR. The processing of geolocation data will require the consent of the data subject in most cases. When basing such processing on legitimate interests, a clear information to the data subject, with proof that it was given, will be required.

Data Protection Impact Assessment

To avoid uncontrollable risks for the data subject’s vital interests and rights, manufacturers and service providers must ensure compliance with the GDPR. Alongside the controller’s obligations to implement technical and organizational measures to assure such compliance, the GDPR establishes dedicated compliance instruments. Namely, controllers can be obliged to perform a data protection impact assessment (DPIA) prior to the start of data processing (Art. 35). The DPIA evaluates the risks arising from the planned processing activities. Such assessment obligation is new and did not exist before the GDPR.

The controller’s obligation to perform the assessment applies “where a type of processing in particular using new technologies, and taking into account the nature, scope, context and purposes of the processing, is likely to result in a high risk to the rights and freedoms of natural persons” and is understood to be required “in case of systematic and extensive evaluation of personal aspects which is based on automated processing, including profiling, and on which decisions are based that produce legal effects concerning the natural person or similarly significantly affect the natural person” or when “processing on a large scale of special categories of data” is carried out.

Data protection impact assessments may have significant impact on the processing activities and should therefore be conducted carefully with involvement of all relevant internal stakeholders (e.g. management, commercial, data protection officer, IT) and external expertise.

Conclusion

Geolocation devices, applications and services are pervasive in an “always connected” world. They have introduced innumerable innovative, profitable and functional services and applications. With location technology, a user’s experience can be uniquely personalized, and user data can be evaluated and processed in a way that was not imaginable a few years ago. This appeals to all types of companies in the digital economy, as well as law enforcement, other public agencies and, unfortunately, criminals.

Compliance with the GDPR is mandatory for all companies falling under its scope, but such compliance can also provide key competitive advantages to other companies. Many countries are preparing for adjustments of the national law according to the European standards, while customers and business partners are increasingly being sensitized to the issue of data protection.

Manufacturers of devices producing geolocation data and services providers processing or using such geolocation data should retain the following:

• Companies basically fall into the territorial scope of the GDPR if:

• their headquarters is located within the EU,

• they have a branch which processes personal data in the EU,

• they offer their goods or services in the EU or

• monitoring of a data subject takes place within the EU.

• The GDPR has a broad definition of personal data. Personal data include all information relating to an identified and even identifiable and living person.

• Once the GDPR is applicable, all its requirements must be met.

• The GDPR’s key requirements regarding geolocation data include:

• Assignment of a data protection officer and, in the case of a company not located in the EU, the designation of a representative in the EU;

• Observance of data subject´s rights (such as Information and data deletion);

• IT Security measures;

• Appropriate safeguards concerning data protection in contracts with service providers;

• implementing and updating a record of data processing activities;

• being able to proof data privacy by design and default;

• performance of data protection impact assessments (if applicable).

The main questions in order to assess on one’s own data protection compliance regarding geolocation data are:

• Are data processed within the EU or do processing activities affect EU citizen data?

• What location data are collected and how are they used?

• Are profiles obtained or derived out of data sets?

• What are the purposes of specific data processing activities?

• What is the legal basis for such data processing?

• Are special categories of personal data (Art. 9 (1)) processed?

• Are appropriate safeguards and technical and organizational measures in place?

• Which information obligations are to be met and how?

• Is there an obligation to perform a data protection impact assessment regarding certain processing activities of geolocation data?

Compliance with the GDPR, if it applies to a company´s activities, is a legal obligation, and non-compliance can lead to severe consequences. Even if compliance with the GDPR is not “legal witchcraft,” it requires awareness and legal expertise in the company. External expertise may be useful to get the process started.

Additional Resources

(1) Text of the GDPR in the current version (all languages): https://eur-lex.europa.eu/legal-content/DE/TXT/?uri=CELEX%3A32016R0679

(2) GDPR guidelines, recommendations and best practices, issued by the European data protection board (edpb): https://edpb.europa.eu/our-work-tools/general-guidance/gdpr-guidelines-recommendations-best-practices_en

(3) First overview on the implementation of the GDPR and the roles and means of the national supervisory authorities, issued by the edpb: http://www.europarl.europa.eu/meetdocs/2014_2019/plmrep/COMMITTEES/LIBE/DV/2019/02-25/9_EDPB_report_EN.pdf

(4) Opinion 13/2011 on Geolocation services on smart mobile devices, issued by the former Article 29 Data Protection Working Party of 16 May 2011: https://ec.europa.eu/justice/article-29/documentation/opinion-recommendation/files/2011/wp185_en.pdf

Authors

Dr. Philip Lüghausen is partner at BHO Legal since January 2019. His practice primarily focuses on data and data protection law with a special focus on scientific and commercial R&D, IT law, E-comm

The post GNSS & The Law: Collecting and Processing Geolocation Data appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Ivan Revnivykh is head of GLONASS Application Division at Roscosmos. His academic career included study and research in France and Italy, and he currently represents GLONASS in numerous international cooperative actions, all of which makes him a familiar face among the global satellite-based navigation community.

“I grew up in the countryside outside of Moscow,” Revnivykh told Inside GNSS. “My home town is Korolyov, near Elk Island National Park.” Not coincidentally, Korolyov is also the home of the Russian space engineering and ISS mission control center.

Revnivykh was born with a passion for GNSS in his blood. His father is the noted GLONASS expert Sergey Revnivykh.Ivan Revnivykh said, “My father started working for the space flight mission control center of the Central Research Institute of Machine Building (TsNIImash) in 1978, before I was born.”

The senior Revnivykh had many important tasks to carry out within the Russian space program, but he made time for his son, sometimes mingling professional and fatherly responsibilities, as when he occasionally took his son with him to the mission control center on weekends. These were important experiences for the youngster, and the memories, Ivan said, have remained with him all through his life.

Thus, young Ivan was always aware of and interested in the work his father was doing. And who wouldn’t be? Since 1995, father Sergey has been in charge of national and international projects in satellite navigation on behalf of Roscosmos, the Russian State Space Corporation.

A willingness to work hard and the determination to solve complex problems were among the traits passed from father to son. “My father was and still is a crazy workaholic,” Revnivykh said with a grin. “He is a man who can become totally involved in the things he deals with. Of course all of my father’s activities while I was growing up left an imprint on me. I’m sure I would not be doing what I am doing today were it not for this early awareness.”

At the far Eastern end of the Asian continent, still in his home country of Russia, Revnivykh spent his childhood summers on Sakhalin Island, on the Pacific Coast. From the age of five Revnivykh passed about 10 summers there, running, climbing mountains, riding bikes and fishing for salmon with his cousins, the children of his father’s sister.

“My father grew up on Sakhalin, in a family of a mining engineers. His father, my grandfather, eventually become a CEO in the island’s coal industry. My grandmother was a mine surveyor, an underground navigation expert, if you will.”

For those not familiar with the remote paradise, Sakhalin Island is a vast, mountainous and heavily forested wilderness, home to bears, foxes, otters, and sables, as well as reindeer and other deer species. There is some isolated but important industrial development, and the island is bounded on its east coast by some of the most productive waters in the North Pacific. “It’s really mad,” Revnivykh said, “Sakhalin is an amazing place, with nature and ocean.

“All in all, it was a very agreeable childhood,” he said. Among other family members who made a big difference were his two grandfathers. “They taught me how to make things by hand, to craft, shoot and hunt. They showed me how to take pictures with old film cameras and to do other boyish things. I had a lot of fun growing up.”

Next Step

Young Revnivykh had an exciting future to look forward to, although he didn’t yet know exactly what kind of work he wanted to do. He was a good student and excelled in the sciences. “I knew for sure that I would be working in a technical field. Anything that involved science and technologies I knew would be a fun area for me to work in. As for my going to university, my parents and I never quarreled. They both supported my decision when I chose the Moscow Aviation Institute.”

In 2002, Revnivykh started worked on two degrees in parallel at the prestigious Institute. He received a bachelor’s degree in Economics in 2007 and a master’s degree in Systems Analyses and Control in 2008, specializing in simulation and operation research in organizational and technical systems. When he graduated, Revnivykh joined Sergey Karutin’s team at JCS (Russian Space Systems) working on the GLONASS System of Differential Correction and Monitoring (SDCM), the Russian correlate to the U.S. Wide Area Augmentation System (WAAS).

Karutin, who is now General Designer of the GLONASS system, is another person who has played a big part in Revnivykh’s life and career. “Karutin is open-minded, very smart and an extremely well organized person who always has an exact target to move towards and to reach for,” Revnivykh said. “For me he is an excellent example of a leader.”

Confirmation

“I could say my ‘GNSS Aha!’ moment came when I participated in the International Summer School on GNSS in 2009, organized by the European Space Agency in Berchtesgaden, Germany,” Revnivykh said. “There were students and young professionals from about 20 countries talking about GNSS as a key component of the world’s information infrastructure, new market capabilities and future innovative trends.”

The exchange of ideas was highly stimulating. One key takeaway for Revnivykh was that in today’s increasingly volatile, uncertain, complex and ambiguous (VUCA) world, satellite navigation requires a truly interdisciplinary approach.

“The Summer School gave me a great opportunity to exchange experience with my international colleagues, to upgrade my skills and to understand what I wanted to do with my professional life. It was all done in an informal atmosphere, and with free drinks! That helped!” Back at work, starting in 2010, Revnivykh became more and more deeply involved in SDCM system design and development. He worked to install three reference stations in Antarctica and one in Brazil. “As a GNSS professional, dealing with SDCM, and especially the work we did in Antarctica, was really pivotal for me,” he said. “I was in charge of deployment of three SDCM reference stations, dealing with preparation, transportation and organization of construction and commissioning works, all with a team of brilliant engineers.”

Revnivykh had to work within the limitations of a short Antarctic summer season, with strict demands in terms of station location and without the ability to study sites visually; the only photos available had been made by polar explorers. The Russian Antarctic station Bellingshausen was chosen as the first site. This was followed by Novolazarevskaya station in 2011 and Progress station in 2012, the last being the most difficult and complex to set up. “In our free time we discovered the surrounding area and taught some penguins to fly,” Revnivykh said. “But seriously, deploying and maintaining any facilities in Antarctica is as challenging as doing the same thing in space.”

New Connections

The following year Revnivykh was back in the classroom, this time in a completely new setting, and his rise on the international scene was about to begin. “I felt that I still had a few things to learn,” he said, “so I enrolled myself in the

Aerospace MBA program at the Toulouse Business School in France. I can tell you, Toulouse was a hard nut. 24/7 studies in an international, multicultural team, with all possible challenges and advantages. The study was in English, but the life was in French, and as I had never studied this language before, I faced some day-to-day difficulties, like dealing with the bank. Kind of important.”

His coursework focused on Northern Sea Route Impact on the Aerospace Industry. “During my studies, I was able to structure my own lessons and make good use of them practically,” he recalled, “bearing in mind the modern international approach to doing business and labor organization. What was really important and great for me was that I made good friends and connections there, with French, Australian and Japanese people.”

It was a period of great personal growth, and of course the fun was never-ending. The program lasted for 14 months, from 2013-2014, five of which were spent as a trainee at Thales Alenia Space in Rome, Italy. “When it was time for the internship in Rome,” he said, “I decided to bring my car down from Moscow. I wanted to be more flexible and to be able to travel in my free time. I did the trip in my car in a week with a good friend of mine. On the way back, by the way, when I’d completed my studies, I nailed the same route in three days, including a stop at the Oktoberfest in Munich. It was a wild party and a great way to draw a line under my Europe studies!”

When in Rome, he did as the Romans did. He ate pizza and joined a rock-climbing club to free his mind and stay fit. And he spent a summer making more of those special memories that last a lifetime.

“I was busy with the optimization of an assembly line of one of telecommunication satellites, O3b and GlobalStar second generation,” he said. “Research undertaken during my traineeship revealed some interesting features not only of satellite assembly but also concerning the impact of human factors in the aerospace industry.

“Rome was very different compared to France. I was working in an office just above a satellite assembly line, with very kind and interesting engineers who really are proud of their job and with an extreme passion for coffee and sun. It was not hard to complete my master’s degree at Thales. The people were open and ready to answer all of my questions.”

Love Chimes In

His academic career at an end, Revnivykh had one more very important task to undertake before moving on with his professional life: getting married. “Following my return from Rome,” he said, still grinning, “a good friend of mine from university organized a party where I met Anna. She was also involved in satellite navigation. As it turned out she even knew my father and had worked with him on some projects. So she was from my world.”

Anna is a graduate of Lomonossov Moscow State University, specialized in linguistics. She currently works in the international department of the Information and Analysis Center for Positioning, Navigation and Timing. She routinely forms part of the Russian delegation at international meetings and committees relating to satellite navigation.

“We were married in 2016 in Korolyov city,” Revnivykh said.”We honeymooned in Vienna, in conjunction with certain parallel United Nations activities: a meeting of the International Committee on GNSS!” The Ivan-and-Anna connection has been a fruitful one, as we shall learn.

Back to Work

“When I came back to Russia,” Revnivykh said, “I worked at TsNIImash for one year and then took up my current position as head of GLONASS Application Division at Roscosmos. My team deals with state R&D within the Federal Program on GLONASS Sustainment, Development and Use.”

Revnivykh is currently working to complete R&D and preparations for the launch of a small retroreflector spacecraft, and he has recently organized and ensured the implementation of R&D projects to create a GLONASS system measuring network (RIMS) on the territory of the Russian Federation and in Antarctica, to provide high-precision navigation for users. He is also charged with implementing results of a wide range of interesting and vital R&D initiatives around the expanded use of GNSS in the transport industry (ERA GLONASS).

Very importantly, Revnivykh coordinates international activities related to GNSS compatibility and interoperability. “Together with leading research institutes,” he said, “we represent the GLONASS system within UN initiatives, such as the International Committee on GNSS and ICAO.” Revnivykh therefore, in a very real way, displays the international face of GLONASS, a cornerstone GNSS program and a true model of excellence, initiative and originality for similar programs around the world.

Foundation Firm

Underpinning all the work he does at Roscosmos, Ivan Revnivykh’s bedrock remains his family. He maintains a good relationship with his parents, and runs into his father very regularly, thanks to their common work in the GLONASS program.”

His aunt still lives on Sakhalin Island, although the cousins he used to run with have moved away, one to Australia, and another to the south of Russia. “We follow each other on social media these days,” he said, “and it’s fine for all of us.”

Closer to home, his wife Anna, at the time of this writing, was on maternity leave; she delivered a second beautiful girl, Daria, in late August.

“Our daughter Elizabeth is two and we are expecting a new baby girl this summer,” Ivan said at the time, grinning more broadly than ever. A new generation of Revnivykhs sets forth on the grand adventure.

Combining life and career is a challenge, Ivan acknowledged, as so many will agree. “I try to spend as much time with my daughter as I can. We ride a bicycle together or go hiking with her sitting in a special kid carrier. I suppose with the second baby, only more and newer challenges are on the way.”

Revnivykh said he does like to apply his engineering skills to his daily, non-working life. “My dream now is to get into CNC [computer numerical control, used in 3D printing technologies] and produce some useful cycling equipment or things for interior design.”

He still likes to get on a bike and ride like the wind. He did a lot of mountain biking in his youth. “I am still into it, now more into Enduro MTB, which delivers the fun!” he said. That’s rugged stuff, with steep uphill and downhill sections.

At the beginning of this profile we posited that Revnivykh was born with a passion for GNSS in his blood, but clearly there is more to him than that. He was born with a passion for life. He has shown it and continues to show it in his work, in his family life, and in all the rest of what he does. The glass is raised — Vashe Zdorovie, Ivan Revnivykh!

As a consumer, what GNSS product, application, or engineering innovation would you most like to see?

Revnivykh said, “I would like to be able to buy a tablet with a real-time zooming world map showing exactly right now all details you need with cm-level accuracy of positioning reference. And it would have some history and predictive functions. Sure, this would not be just a GNSS technology, but would need synergy with satellite remote sensing and communication as well.”

The post Human Engineering: Ivan Revnivykh appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

Seventeen years after federal regulators restricted a promising wireless technology to protect GPS and other spectrum users, they are being asked to loosen those limitations. Ultra-Wide Band (UWB) proponents insist the strictures are too tight and cut off their ability to innovate.

In June, Robert Bosch LLC formally requested the Federal Communications Commission (FCC) do what it said it would do back in April 2002: reexamine controls on UWB that the FCC itself called “extremely conservative” when they were written. The request is not to change the rules—not just yet anyway—but for the FCC to launch a rulemaking process that could eventually lead to changes in the rules. And Bosch wants a lot of changes.

Expand Ultra-Wide Band?

The company seeks to alter the definition of UWB, to allow more frequency-hopped emissions, stepped frequency and swept frequency
(frequency modulated continuous wave or FMCW) emissions. It is unreasonable, Bosch said, to continue to preclude all frequency-hopped UWB emissions below 10 GHz as well as pulsed, stepped and swept emissions.

The firm also wants to change the kinds of applications the rules allow. For example, it said, there is potential for using UWB imaging and surveillance systems for ranging, tracking and object classification; there are also opportunities in the home and building automation sectors for motion and presence detection. Bosch noted the EU allows UWB for fixed outdoor applications and location tracking. Bosch also said the use of UWB imaging and surveillance should not be limited to law enforcement, fire/rescue organizations, public utilities, mining and construction companies.

“A more generalized definition for UWB surveillance system applications is in order,” the firm said. “Within these more generic definitions should be included more flexible use cases (independent of user categories, which do not by themselves avoid misuse). If the definitional regulations are not preclusive of new UWB applications, and if there are clear, yet flexible technical requirements (including the flexibility to utilize varied emissions types), this would provide better interference protection than defining who the eligible users of unlicensed devices are and excluding most use cases in the process.”

Surveillance and imaging applications are not the only ones being unduly restricted, Bosch suggested. The firm noted that UWB technology can’t be employed for generic material sensing or the operation of toys. UWB operation is not allowed inside vehicles, onboard aircraft, ships or satellites.

Of the large number of potential UWB devices and applications, NTIA and others have tested only a small number, and those test results cannot be extrapolated to include, for example, the total power of a UWB communications network, or the aggregate, cumulative effect of UWB devices.” – Air Transport Association and affiliated organizations including the U.S. GPS Industry Council

Some of the controls, Bosch insisted, don’t make any sense. For example, while it’s OK to use UWB to determine what is inside a wall, it is not OK to use UWB as a stud finder.

“The current UWB rules (principally but not exclusively the technical and definitional rules) are so stringent,” Bosch wrote, “that of necessity, virtually all new UWB products and systems must apply to the Commission for, and be subject to a waiver as an incident of being granted certification for marketing and sale of the device in the United States.”

In the Beginning

Those rules took shape during a fight started by the FCC itself in October 1998, when the agency launched an inquiry into whether UWB devices could be allowed to operate on an unlicensed basis under the Commission’s Part 15 rules. UWB uses very low power signals and precisely timed pulses—rather like the ping of a tuning fork—to transmit data, sense objects or do unusual tasks like “see” through walls. UWB capabilities are enabled, in part, by the fact that its signals range across hundreds, sometimes thousands, of megahertz of frequencies. But because the signal power is low, and the pings short-lived, the chance of interference is lower.

While the risk to many existing uses of radio frequencies may be limited, there was almost immediate concern about UWB’s potential to impact another low-powered signal: GPS.

The Federal Aviation Administration (FAA) was one of the first to respond to the FCC’s 1998 inquiry, saying that allowing UWB systems to operate in restricted bands would degrade the safety of the nation’s air traffic control system.

“In the case of the FAA, the restricted bands are allocated to services used for critical aeronautical safety services,” the agency wrote. “The FAA is opposed to any authorization of licensed or unlicensed UWB systems to intentionally radiate in these bands. It is likely that authorizing even limited operation of such systems will lead to further proliferation of UWB systems as new applications for their use are developed. Civil aviation’s requirement for protected radio spectrum, including that supporting critical communications, navigation, and surveillance safety services, is paramount.”

The GPS Industry Council expressed concern early on that operation of UWB systems in bands that overlap with the GPS frequency bands would cause interference with a wide range of safety-of-life applications. “Any increase in the basic noise floor will significantly reduce the ability of the receiver to acquire a GPS signal or even to maintain tracking of a GPS signal, or cause errors in position or time accuracy. Any of these consequences is intolerable to the GPS user segment.”

Over the course of the next four years—in a process that Bosch called “contentious”—the GPS community and a host of users of other bands argued with UWB advocates about opening the airwaves to the new technology. A total of 1,167 documents were filed in a docket that was finally closed in 2014.

In the case of GPS, the discussion evolved into hardware testing. The National Telecommunications and Information Administration (NTIA) tested a series of GPS receivers to determine how they would stand up if the satellite navigation bands were used for a range of different UWB applications. NTIA is the coordinating agency for government uses of the spectrum—which includes the frequencies relied upon by the Air Force-run GPS constellation—and its input carries significant weight. The results informed restrictions on UWB frequencies that barred nearly all applications from using GPS bands, limited emissions and put in place other restrictions when the FCC issued its First Report and Order in 2002.

Getting to the testing process, however, was not all that smooth. The FCC said, at first, that it would allow time for testing and then asked for results in four months, which experts said was not enough time for full studies. Initially there was no money budgeted for the testing, some of which was also done by Stanford University, the University of Texas and the Applied Physics Laboratory working in cooperation with Johns Hopkins University. Thirty days into the 4-month test period a total of $600,000 was scraped together by the FAA and its parent agency, the Department of Transportation, only to have a member of Congress step in and freeze the transfer of the funds. Senator Richard Shelby (R-Alabama), then the head of the Senate Appropriations Subcommittee on Transportation, called the FAA Administrator and asked that the funds be held pending the development of fair and effective parameters for the tests. Time Domain, one of the key UWB players involved in the rulemaking, was a constituent of Shelby’s, who is still in Congress and now holds one of the most powerful jobs in Washington: chairman of the Senate Appropriations Committee. In 2018 Time Domain became part of Humatics, a microlocation technology company which maintains an office in Huntsville, Alabama, where Time Domain was based.

The military also weighed in on allowing UWB. Deputy Secretary of Defense Paul Wolfowitz wrote to the Secretary of Commerce saying, “UWB must be regulated in a way to protect military and civil use of GPS and other critical systems.” According to references in the filings, DOD took the position that it was necessary to limit UWB devices to operating at 4.2 GHz or above, except, perhaps, for imaging devices. Sprint PCS said in a filing that it was aware of similar concerns about harmful interference to various other services, suggesting that the FCC could provide adequate protection to all existing services by limiting UWB emissions to above 6 GHz.

Testing, Testing

The tests found that UWB signals could interfere with GPS. The FCC analyzed the results across more than 16 pages of its First Report and Order, which was posted in the docket May 17. In the end, the Commission concurred that extra care was needed to protect GPS users.

“Of particular concern is the impact of any potential interference to the GPS band at 1559–1610 MHz,” the FCC wrote. The Commission also expressed concern about “interference to any additional frequencies allocated to GPS, e.g., the planned L5 frequency in the 960-1215 MHz band. GPS will be increasingly relied upon for air navigation and safety, and is a cornerstone for improving the efficiency of the air traffic system. GPS also may be used by commercial mobile radio E-911 services to enable police and fire departments to quickly locate individuals in times of emergency. Moreover, businesses and consumers are now employing GPS for various applications, such as for navigation by automobiles, boats and other vehicles, surveying, hiking, and geologic measurements. Therefore, any harmful interference to GPS could have a serious detrimental impact on public safety, businesses and consumers. In addition, propagation losses are not as great below 2 GHz, and services in this region of the spectrum tend to employ omnidirectional antennas that do not discriminate against undesired signals. These factors tend to increase the risks of interference below 2 GHz.”

To address these risks but still allow some the use of UWB, the FCC devised a complex set of application-by-application, band-by-band restrictions including limits on the Equivalent Isotropically Radiated Power (EIRP) of out-of-band emissions (OOBE). For example, through-the-wall imaging systems—which, as the name implies, enable users to determine the location or movement of people or objects on the other side of a wall or similar structure—were only allowed to operate below 960 MHz or in the frequency band 1.99-10.6 GHz. The OOBE into in the 960-1610 MHz band from these devices had to be kept at or below –53.3 decibel-milliwatts (dBm) and –51.3 dBm or less in the 1610–1990 band. Their use was limited to law enforcement, fire and rescue organizations.

Systems that image what’s inside walls or structures like the side of a bridge or the wall of a mine must operate below 960 MHz or in the frequency band 3.1–10.6 GHz. Their operation is restricted to law enforcement, fire and rescue organizations and to scientific research institutions, commercial mining companies and construction firms.

High-frequency systems operating with a –10 dB bandwidth between 3.1 GHz and 10.6 GHz—which includes some ground penetrating radars (GPRs), communications and measuring systems as well as wall and medical imaging devices—need to keep the EIRP of their OOBE to –65.3 dBm or less in the 960-1610 band and at –53.3 dBm or less in the 1610–1990 band. This class of UWB imaging systems also requires government coordination.

What’s Next?

The importance of doing more tests as an element of expanding future UWB usage was highlighted during the original debate by a coalition of more than three dozen firms and organizations including the Air Transport Association, several airlines, GPS receiver manufacturers, surveyors, telecommunications firms, and the U.S. GPS Industry Council.

“The broad range of prospective UWB devices includes many different types of signals in many potential bands and their measurement and analysis is very complex,” the coalition wrote, on March 27, 2001. “However, effective and reliable measurement of the emissions from these devices is critical to assess whether and how UWB pulse position modulation technologies can be successfully and safely introduced into the frequency domain without life threatening or other adverse effects on existing operations. Of the large number of potential UWB devices and applications, NTIA and others have tested only a small number, and those test results cannot be extrapolated to include, for example, the total power of a UWB communications network, or the aggregate, cumulative effect of UWB devices. So, despite the testing done to date, adequate measurement and analysis of the effect of the full range of potential UWB devices on other spectrum users has not yet been done.”

Protecting incumbent spectrum users isn’t just about testing the different UWB applications, but also about assessing how many UWB devices there will be and where they will be, wrote Per Enge, then an associate professor at Stanford and part of its GPS Lab.

“While one, or even a small number of such UWB interference sources may cause only a small degradation in the noise level of wireless communications receivers, as well as GPS receivers…” Enge wrote in comments in September 1999, “large numbers of such UWB interference sources distributed over an area can raise significantly the overall noise level of all receivers in the area. Such a noise level increase will significantly reduce the sensitivity of the receivers and with it wireless communications system range and GPS performance.”

Moreover, he said, the increase in the noise floor caused by a UWB device at some distance from a GPS receiver may be small but grows as the separation decreases.

“With proliferation of UWB sources throughout an area, the likelihood of at least one UWB interference source being close enough to any particular wireless communications or GPS receiver to cause a significant increase in its noise level increases significantly.”

The GPS Innovation Alliance (GPSIA) argued this August against opening a new rulemaking as Bosch requested, saying that there has been no change in the technical analysis underpinning the existing UWB rules since they were created in 2002. Indeed, the suggestion that UWB devices have been able to operate without creating interference or raising the noise floor is not an argument against the need for strict rules, as Bosch suggested, but proof that the existing limitations are working, GPSIA wrote.

Moreover, some of the more “generic” rules being sought by Bosch would open the door to much higher OOBE levels, GPSIA said. The EIRP in the 1559–1601 MHz band would jump by up to “a staggering 29 dB” for vehicular radar, an application the FCC expected in 2002 to become “as essential to passenger safety as airbags.” That jump is “certainly not ‘negligible,’ ” GPSIA wrote.

Given the potential for dramatic increases in OOBE in critical safety-of-life bands, GPSIA said, and the lack of any technical analysis in the petition supporting the assertion that added UWB operations would not create harmful interference, new rules that facilitate the en masse introduction of more “generic” UWB devices “do not merit further consideration.”

GPS is a critical service to both civil and military users, GPSIA said in its filing, and has generated an estimated $1.4 trillion in economic benefits since it was made available for civilian and commercial use in the 1980s.

“Most benefits have accrued in the last 10 years and are spread among many major commercial sectors in the United States that have adopted GPS including 14 of 16 industries deemed infrastructure,” GPSIA wrote. Interference issues are, “if anything, even more important today given the natural proliferation of GPS and GPS-enabled devices embedded in practically all walks of American life.”

“The potential of UWB devices to interfere with GPS was (and continues to be) well established. Based on the robust record, the Commission prudently chose a conservative approach to ensure interference from UWB devices will not degrade the performance of GPS signals,” said GPSIA.

There is nothing new in the technology or the petition that argues for changing those rules, GPSIA said, and loosening the limitations, as proposed by Bosch, creates risks. “The absence of an increased noise floor or a rise in interference is hardly a reason to start a rulemaking to overhaul the rules” GPSIA wrote; “once that damage is done, it cannot be undone.”

Dee Ann Divis has covered GNSS and the aerospace industry since the early 1990s, writing for Jane’s International Defense Review, the Los Angeles Times, AeroSpace Daily and other publications. She was the science and technology editor at United Press International for five years, leaving for a year to attend the Massachusetts Institute of Technology as a Knight Science Journalism Fellow.

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The focus of that effort is on PNT as it relates to critical infrastructure, which is in line with NSPD-39: U.S. Space-Based Position, Navigation, and Timing Policy (National Security Presidential Directive-39). For more details on NSPD-39, see the Federation of American Scientists’ Fact Sheet.

“I’d like to read you just a few things that our secretary, (DHS) Secretary (Kirstjen) Nielsen said regarding critical infrastructure and the world that we’re in today,” Platt told attendees at the Civil GPS Service Interface Committee (CGSIC) meeting Tuesday.

She told those attending the standup of the National Risk Management Center in September that in the months prior to 9/11 then-CIA director George Tenet said the systems were blinking red. “We’d heard enough chatter to know that danger was coming we just didn’t know where.” Recently Dan Coats, the director of the National Intelligence Agency, said something similar, Nielsen continued.” “The system is ‘blinking red once again. …Our digital lives are in danger like never before. But it’s more than that; we are witnessing an historic change across the entire threat landscape.’ ”

“So that’s the environment that we’re facing right now,” said Platt. “It’s not just about the hackers that are going to put something out there and hack into the local system. We have to realize that there are nation states that are willing to target our critical infrastructure.”

To address this, Platt’s team — which is now part of that new National Risk Management Center — is working on a risk management structure and reaching out to industry to establish partnerships.

“Over 90 percent of the critical structure in the United States is owned by the private sector,” Platt said. “So, if they’re using GPS and they’re dependent on GPS — or heavily reliant — it’s up to us to help them understand the risk. But ultimately those companies are going to make decisions on the overall risk picture that they’re facing and we have to make a compelling argument that they should at least address the PNT vulnerabilities that potentially exist.”

To do that Platt’s team is doing testing to help industry understand those risks. They are sharing best practices with manufacturers and end users. He also mentioned the possibility of more advanced steps like having standards for certain receivers and possibility of a backup system.

“We can’t back up GPS for everything. If we could — if we could field a system that matched GPS in every aspect and we could do it on a budget that’s less, then why would we need GPS? The answer’s pretty simple; we can’t do that. We have to find ways of identifying what’s critical, making sure there’s security and resiliency in those critical systems.”

The 58^thmeeting of the Civil GPS Service Interface Committee (CGSIC) meeting took place Monday and Tuesday in conjunction with the ION GNSS+ 2018 technical meeting.

The post DHS Taking a Risk-Based Look at PNT Resilience appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

The signal composition and general architecture of the proposed software receiver implementation are provided along with detailed descriptions of the main signal processing algorithms involved in acquisition, tracking, and telemetry decoding of the navigation signal. Connections to external software by means of traditional format output standards are also presented and validated with real-life signals.

The transmission of new radio navigation signals in a dedicated band has revolutionized the GNSS industry by allowing for crosscompatibility and reduced expenses in receiver design. The concept started with the transmission of the Global Positioning System (GPS) L1 C/A signal, which soon after became the gold standard of radio navigation. GLONASS satellites followed and the constellation reached maturity during the Soviet Union era, but degraded after its collapse. The Galileo constellation finally cemented this idea with the addition of the E1 open service signals. Such efforts prepared the field for international collaboration and signal design around the concept of multi-constellation receiver designs.

The Government of the Russian Federation approved by its Decree No. 587 of 20 August 2001, a budget of 347 billion ruble (US $11.81 billion), running through 2020 by which a federal task program will restore and modernize the GLONASS constellation. The program aims at improving the space, groundbased, and user equipment segments of the system. By 2010, the constellation reached full coverage in Russia and in 2011 full operational capability when the full orbital constellation of 24 satellites was achieved. Aiming to provide better accuracy, multi-path resistance, and especially, greater interoperability with GPS, Galileo, and other GNSS, new GLONASS-K satellites will transmit Code Division Multiple Access (CDMA) signals in addition to the system’s traditional Frequency Division Multiple Access (FDMA) signals. GLONASS system restoration is almost completed and the latest updates seem to indicate that the program is on track and has enough budget to complete its modernization in the future (see Additional Resources, I. Revnivykh). The new modernized system will ensure that GLONASS coherent FDMA and CDMA navigation signal sets will satisfy a wide range of user requirements, from ordinary navigation to high-precision applications.

With the international community moving in this direction, it is more common nowadays to see receiver development focused on CDMA techniques targeting single bands and simplifying the receiver design. Given all this, it is worth asking: Is there benefit in GLONASS FDMA signal processing? What are the advantages of a navigation system processing GLONASS FDMA signals?

Previous research highlights feasibility and effectiveness of cheap jammers on radio navigation signals (T. Kraus et alia; A. D. Fonzo et alia). It can be speculated then that a system moving toward a single band navigation system by means of CDMA exploitation is also extremely susceptible to commercial off-the-shelf jammers and Personal Privacy Devices (PPD). An analysis of commercial offthe-shelf units and PPDs showed how these devices can turn a wide range of CDMA signals completely unusable in their presence (T. Kraus et alia). Interestingly enough, of the seven devices studied, only 10% were capable of blocking the bands where GLONASS and its frequency channels operate (Tables 1 and 2). Taking advantage of the processing of GLONASS FDMA signals is not a direct anti-jamming or anti-spoofing technique, but use of these signals does make it harder to harm the system.

Another interesting case was the recently reported episodes of GPS spoofing happening in the Black Sea (S. Goff). Given the resources available, receivers can no longer simply rely on one single constellation or the other. The future lies in the design of receivers capable of mixing solutions from multiple constellations in a wide range of frequencies. Maybe the presence of a GLONASS capable receiver would have avoided the spoofing by allowing the system to eliminate the compromised GPS measurements and perform navigation with the aid of the GLONASS FDMA signals, assuming of course that the latest signals were not also

spoofed in the area during those episodes.

This work presents a new signal addition to the Global Navigation Satellite System Software Defined Radio (GNSSSDR) platform, making the receiver more robust and diverse. With the GLONASS L1 C/A signal addition, the GNSS-SDR software receiver is available for a new set of applications based on the use of the GLONASS FDMA signals. Given the points made earlier, it seems that receivers taking advantage of a new signal could be better prepared against malicious attacks of any kind. This work is not of course the first implementation of the GLONASS L1 C/A signal and there are multiple implementations by commercial off-the-shelf receivers that support this signal without major issues. Such commercial receivers come with a high price tag that could be a barrier in low funded applications or studies. Some open source versions also support the GLONASS L1 C/A signal, like GNSSSDRLIB, but its software implementation is limited to only the Windows 64 bits platform, does not takes advantage of the latest Intel Advanced Vector Extensions (AVX), and does not support its exportation to available embedded platforms (T. Suzuki and N. Tubo).

GNSS Software Receivers

Since the advent of the Software Defined Radio (SDR), an increasing number of GNSS software receivers have been developed by members of the navigation community. Typical implementations will use low level programming languages like C or C++ in order to achieve a real time receiver running in a general

purpose computer or an embedded platform. Another design approach implements the receiver in a high level programming language like MATLAB or Python, and develops a software receiver ideal for post-processing applications. Popular and open source implementations include GNSS-SDR, GNSSSDRLIB, and SoftGNSS. GNSS-SDRLIB, mainly developed by Taro Suzuki was developed in C and uses the RTKLib navigation engine for computation of the position solution (T. Suzuki and N. Kubo). The software receiver supports all major constellations and signals providing acquisition, tracking, and pseudorange metrics for post-processing analysis. This software receiver, however, is no longer under active development. On the other hand, softGNSS is a software receiver for post processing analysis developed in MATLAB, source code of the receiver is provided with the book that introduces it (K. Borre et alia), and it provides code for a GPS L1/CA signal receiver. Even though multiple efforts have been directed towards extending the receiver capability with the addition of new signals and features, its MATLAB implementation does not make this software receiver adequate for real time signal processing. Another important contribution to the community comes with the software receiver developed by Thomas Pany and his book on GNSS signal processing (see Additional Resources), which introduces key concepts of GNSS software processing in personal computers and discusses several common techniques that can be used to achieve real time processing. In the author’s opinion, a major contribution of this book was the usage of assembly language to accelerate common operations on GNSS signal processing.

There are also commercially available software receivers for use in the community. The business approach for this concept mostly includes a combination of IP core licensing and/or royalties for its use. The commercial implementations will serve multiple applications such as multipath and spoofing evaluation, ionosphere scintillation, interference monitoring, etc. One such software receiver version is capable of running in real time more than 300 channels in a general purpose computer with an Intel Core i7-7490k processor. The solution also provides a hardware front end for data collection and is integrated with the software receiver provided (see Manufacturers section near the end of this paper). Other options will provide free academic versions (normally introduced with a textbook) (I. Petrovski and T. Tsujii) and will then charge for a professional version of the software, including one which will also have the option of a custom Radio Frequency (RF) front end to interface with the software receiver. The result of years of experimentation with software radios as applied to GNSS technologies is a well-established set of tools for the scientific community with plenty of options to pick from depending on end applications, budgets, and experience in the field of radio navigation.

GNSS-SDR

Development of the work presented here was accomplished with GNSS-SDR. This is an open source receiver developed in C++ that uses the GNURadio Application Programming Interface (API) to develop a real time software receiver. The high level of flexibility and re-configurability makes this implementation a very appealing solution to an ever increasing radio navigation signal environment. GNSS-SDR provides an interface to different suitable RF front-ends and implements the entire receiver chain from signal reception up to the navigation solution. Its design allows any kind of customization, including interchangeability of signal sources, signal processing algorithms, interoperability with other systems, and output formats, and offers interfaces to all the intermediate signals, parameters, and variables (C. FernándezPrades et alia, 2011). GNSS-SDR runs on a personal computer or an embedded platform and provides interfaces through Universal Serial Bus (USB) and Ethernet buses to a variety of either commercially available or custom-made RF. As an object-oriented platform and with the idea of keeping a sense of abstraction in the blocks developed, GNSS-SDR is divided into several processing blocks that participate in the whole process of navigation for receivers. These blocks (Figure 1) are part of the abstraction level in the software and can be divided as follows: 1) Signal Source Blocks: Hide the complexity of accessing each specific signal source, providing a single interface to a variety of different implementations. 2) Signal Conditioner Blocks: Adapt the sample bit depth to a data type tractable at the host computer running the software receiver, and optionally intermediate frequency to baseband conversion, resampling, and filtering. 3) Channel Blocks: Encapsulate all signal processing devoted to a single satellite. This is a large composite object which encapsulates the acquisition, tracking, and decoding modules. 1) Acquisition Blocks: Provide a coarse estimation of two signal parameters: the frequency shift (f d ) with respect to the nominal Intermediate Frequency (IF) frequency, and a code delay term (τ) of in view satellites relative to the shifted version of the local code replica. 2) Tracking Block: Aims to perform a local search for accurate estimates of code delay and carrier phase, with their eventual variations based on the input provided by the acquisition block. 3) Telemetry Decoder Block: Detects and decodes the navigation message containing the time the message was transmitted, orbital parameters of satellites (ephemeris), and an almanac. 4) Observables Block: Collects all the data provided by every tracked channel, aligns all received data into a coherent set, and computes the observables (pseudorange, carrier phase, etc.). 5) Position Velocity and Time (PVT) Block: Computes the position solution of the receiver based on all the data generated by the previous block.

Extending the Receiver to GLONASS Processing

Due to its different layers of abstraction, prototyping of the GLONASS L1 C/A signal was done rapidly in the code base. Figure 2 shows the blocks of code added to the system and its object oriented properties relationship with the core blocks of the receiver. Note that the figure only displays the blocks created or modified for GLONASS L1 C/A, and is by no means a detailed Unified Modeling Language (UML) diagram of the objects present in the platform. It is worth mentioning that the GNSS-SDR platform uses some of the key concept ideas of GNU Radio in the sense that it has an abstract block upon which all other interfaces and implementations are based. The block GNSSBlockInterface defines a set of basic properties common to all objects that inherit its properties. Direct descendants of this GNSSBlockInterface are interfaces that describe the software receiver, which range from a Channel interface to a PVT interface that serves as the top layer of the software. Once the basic layers of the GNSS-SDR are defined, then the adapter blocks are used to implement the basic methods of these interfaces and define some others if required (C. Fernández-Prades et alia, 2012). This in particular allows for an extremely flexible design in which adapter blocks could be swapped depending on the algorithm or signal to be processed. At the same time, each adapter block’s dependencies connect GNSS-SDR with the GNU Radio API inheriting some functionality of the gr::block upon which the entire GNU Radio platform is defined.

GLONASS FDMA Signal Model

GLONASS satellites orbit Earth at a 64.8 degree inclination (GPS uses six planes at 55 degrees). This inclination is ideal to ensure good coverage of polar latitudes, where a significant portion of the Russian Federation territory is located. The satellites have an altitude of around 19,100 kilometers in a nearly circular orbit with eccentricity near 0 (again, see Additional Resources). The previous orbital parameters make the satellites have an orbital period of 11 hours, 15 minutes, and 28 seconds, with repeating ground tracks every 7 days, 23 hours, 27 minutes, and 28 seconds. As mentioned before, the GLONASS system uses FDMA for its C/A signal. The system now has allocated 14 frequency channels in the L1 band that are spaced from each other with a constant frequency offset. The received signal can be described as per Equation (1): where: PC is the power of signals with C/A code, C k is the C/A code sequence assigned to satellite number k, τ is the code phase received in ground, Dk is the navigation data sequence, f kL1 is the nominal value of the FDMA L1 carrier frequencies, f d is the Doppler frequency seen in the ground, and n is the received noise. The nominal values of the FDMA L1 carrier frequencies are defined by Equation (2): where: k = represents the frequency channel, f 0L1 = 1602 MHz for the GLONASS L1 band, and ∆f L1 = 562.5 kHz frequency separation between GLONASS carriers in the L1 band. Since a total of 24 satellites populate the constellation and only 14 frequency channels are available, GLONASS satellites will share some frequency channels but only when in antipodal positions, i.e., satellites in opposite position on Earth. General parameters of this signal are defined in Table 2.

Signal Source & Signal Conditioner

The Signal Source block hides the complexity of accessing each specific signal source, providing a single interface to a variety of different implementations. Each implementation will target the parsing of data from specific front ends or custom formats in a raw file. It will then transform it to the standard used by the receiver. Using the NT1065 front end (N. C. Shivaramaiah et alia) data was collected across the GLONASS L1 frequency band and minor modifications to the signal source blocks were added to parse the data previously stored in a file. The Signal Conditioner block oversees adapting the sample bit depth to a data type tractable at the host computer running the software receiver, and optionally intermediate frequency to baseband conversion, resampling, and filtering. Regardless of the selected signal source configuration, this interface delivers a sample data stream to the receiver processing channels, acting as a facade between the signal source and the synchronization channels.

Acquisition

The role of an Acquisition block is the detection of signals from a given GNSS satellite. As per Equation (1),

in the case of a positive detection, it should provide coarse estimations of the code phase (τ) and the Doppler shift (f d ) to initialize the delay and phase tracking loops. Since GLONASS FDMA uses a gold code to detect time delay, acquisition techniques developed for GPS L1 C/A were modified to accommodate GLONASS processing. GLONASS signal acquisition can be seen as that of GPS L1 C/A, but instead of looping over different Pseudo Random Noise (PRN) code values, the code will loop over a single code at different frequency channels k. After frequency channel removal, the typical Parallel Code Phase Search (PCPS) algorithm (D. Akopian) (Figure 3) can be used for acquisition. Most importantly, this approach reuses previously developed blocks in the platform, which allows for this flexible level of abstraction within the internal software architecture. Figure 4 shows the acquisition results for a GLONASS satellite in real data collection. The significant peak in the figure indicates a positive signal detection on a frequency channel.

Tracking

The Tracking block is also receiving the data stream xIN, but does nothing until it receives a “positive acquisition” message from the control pane, along with the coarse estimations τacq and f dacq. Then, its role is to refine such estimations and track their changes along time. Three parameters are relevant for signal tracking: the code phase (τ), Doppler frequency (f d ), and carrier phase (ψ). As with the signal acquisition, GLONASS L1 C/A signal tracking reuses blocks developed for the legacy GPS L1 C/A signal tracking. The main difference to consider is the removal of the frequency channel offsets from IF due to its FDMA properties. After carrier removal happens, tracking for GLONASS could be treated as a typical GPS L1 C/A tracking module (Figure 5). Re-usability of existing

blocks reduces code complexity and highlights the benefits of flexibility within the platform. Tracking results are shown in Figure 6. The values for the C/ N0 , carrier frequency (relative to nominal frequency channel), and code frequency are shown in the bottom, while a discrete time scatter plot showing phase lock with bits of navigation is shown up top. Due to the effect of the meander sequence present in the GLONASS Navigation Message (GNAV) message (see Additional Resources), bits of navigation need further processing before decoding can be applied to the signal. WORKING PAPERS FIGURE 3 Generic PCPS acquisition implementation in GNSS-SDR Incoming signal Output Buffering Local oscillator Circular Shift Gold Code IFFT 1 | 2 FFT FFT 90 deg conj Q r FIGURE 4 Acquisition Results for GLONASS Satellite Number 22 in the GNSS-SDR platform Code Delay (chips) 0 –10000 –5000 0 5000 100 200 300 400 Dopler Shift (Hz) Acquisition metric (µ) ×1013 18 16

Telemetry Decoding

Telemetry Decoding block decodes the navigation message for the signal. Once the signal is properly tracked , the Tracking block will start to populate the required fields in the gnss_ syncro object, which ser ves as the pipeline between the implementation of the blocks in the channel. The symbols populated in gnss_syncro will be used to decode the GNAV message after the meander sequence has been removed, as shown in Figure 7. GNAV decoding is a very straightforward process that requires careful bookkeeping between the bit position for each of the fields in the string, which is carefully described in its Interface Control Document (ICD) (see Additional Resources). Following the design pattern of GNSS-SDR, the decoded data was divided into four objects, named Glonass_Gnav_Ephemeris, Glonass_Gnav_Navigation_Message, Glonass_Gnav_Utc_Model, and Glonass_Gnav_Almanac, holding the relationships described in Figure 2.

Observables

The Observables block generates by default three types of measurements for processing in the navigation solution computation. These measurements include pseudo-ranges, accumulated carrier phase, and Doppler frequencies. All of this is generated through a single object called Hybrid_Observables, which computes these measurements in a generic way across all channels. As such, work in this area was minimalistic and only consisted of managing the information flow between the Telemetry Decoder and Observables block. Nevertheless, a proper definition of the measurements as per the GNSS-SDR platform is offered for clarity. Pseudorange Measurements The basic observation equation for the pseudorange as given by K. Borre et alia, assuming that is geometric pseudorange from satellite k to the receiver i, c is speed of light, δti is receiver clock offset, δtk is satellite clock offset, is tropospheric delay, and is ionospheric delay, is Accumulated Carrier Phase Measurements Following the same definition of pseudorange, the carrier phase can be defined as a measurement of ranges as defined in Equation (4) The last term of the equation represents the initial unknown ambiguity in the cycle number relating to the distance between receiver and satellite. However, when applying a differentiation of continuous carrier phase measurements, ambiguities in the number of cycles are eliminated due to the fact that for continuous carrier phase measurements, the ambiguity will be the same in both cases. The result is a measurement of the range rate defined as:

Doppler Shift Measurement

The Doppler effect is the change in frequency for an observer (in this case, the GNSS receiver i) moving relative to its source (in this case, a given GNSS satellite k). Equation (6) gives the relationship between observed frequency f i and emitted frequency f k : Since the speeds of the receiver vi(t) and the satellite v k are small compared to the speed of light c, the difference between the observed frequency f i and emitted frequency f k can be approximated by Then, the Doppler Effect measurement can be written as where r r (t) and vr (t) are the position and velocity of the receiver at the instant t. The term v (s)(t(s)) − vr (t r ) T is the radial velocity from the receiver relative to the satellite, and and are the receiver and satellite clock drift, respectively. The Doppler Effect measurement is given in Hz.

PVT

The PVT solution in GNSS-SDR currently uses a module created based on the RTKLib library. As such, the GLONASS integration to this module was only in charge of providing the necessary conversion tools to translate from
the GNSS

-SDR modules to the RTKLib input. All conversion parameters were developed following the description of the RTKLib API library (T. Takasu and A. Yasuda). To test the code developed and presented in this work, a GNSS data logger using the NT1065 front end was used. The data logger of this device allowed for mult

i-frequency, multiband collection at the same time. For this study, the specific configuration loaded in the device’s firmware targeted a data collection for GPS L1 C/A, GLONASS L1 C/A, GLONASS L2 C/A, and GPS L2C, simultaneously. Figure 8 shows the first position solution for GLONASS L1 CA signals ever achieved by GNSSSDR. The figure shows the position of the receiver in the Earth Centered Earth Fixed (ECEF) coordinate frame with the position covariance for each of the components. The figure also plots the clock variation error, the number of observations used during the position computation, and the estimated position across the X and Y components relative to the true antenna position. Finally, the 90% Circular Error Probable (CEP) statistic, as per the description by G. M. Siouris, is shown. Case Study: Performance under RFI A Radio Frequency Interference (RFI) tone was introduced in the data set collected for the GPS L1 C/A signal, simulating a scenario where a common Continuous Wave (CW) PPD device will null the operating band of the signal. Typical RFI devices, such as those described in Table 1, were studied and a pulse mimicking their behavior was generated to null the band. The simulated interference in the band was inserted 60 seconds into the collected data (allowing initial position solution computation) and lasted for about 20 seconds. Figure 9 shows the position solution generated by GNSSSDR under the presence of a nulling RFI tone. After 60 seconds of data processing, the position solution stops, resuming around 50 seconds later. This indicates the inability of the receiver to compute its position. Also of interest is the fact that after the RFI resumes, the receiver will need to spend time to decode the ephemeris data before being able to compute a position solution unless some intelligent signal processing technique is applied to reuse the previous decoded ephemeris. If under the assumption of this experiment, a PPD device as in Table 1 is used, then the GLONASS FDMA signals could be used to keep the position solution active, even during the jamming period. Results of this scenario are shown in Figure 10. The receiver, when using a combined solution of GPS L1 C/A and GLONASS L1 C/A, is able to provide position estimates even though the RFI tone is nulling the GPS L1 band. Due to the reduction in the number of observations when the GPS L1 band is nulled, a small performance hit is seen but no loss of the position solution happens, which is the cumbersome mission of the proposal.

Conclusion

This article presents the first-ever position solution of GLONASS L1 C/A in the GNSS-SDR platform. This addition will allow the receiver to take advantage of the FDMA signals available in the radio navigation spectrum and will open a new set of tools for the scientific community to use in the diverse field of GNSS processing. Addition of the GLONASS L1 C/A in a combined position solution is a simple but effective technique to aid the overall position solution of a receiver when in the presence of RFI or spoofing attacks. We also present a detailed description of the process of signal addition to the GNSS-SDR platform and serves as a template for developers with the intention of contributing to the platform.

Acknowledgments

This material is based upon work partially supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE 1144083 and the Google Summer of Code (GSoC) 2017 program. Special thanks to the GNSS-SDR team mentors during the GSoC 2017 program including Luis Esteve, Carles Fernandez–Prades and Jordi Vilà Valls. In addition, special thanks to Professor Dennis M. Akos for providing some of the data sets used during the testing stage and to the editors of this manuscript Dr. Nagaraj Channarayapatna Shivaramaiah, Sara Hrbek, and members of the University of Colorado at Boulder Wri

ting Center. Manufacturers The first software receiver described in the GNSS Software Receivers section of the article and in Additional Resources (Pany et alia, 2012) is the SX3 software receiver developed by IFEN GmbH from Poing, Germany. Another commercial receiver discussed in the section was the ARAMIS from iP-Solutions with offices in Japan, UK and the U.S. Additional Resources 1. Akopian, D., “Fast FFT based GPS Satellite

Acquisition Methods,” IEE Proceedings – Radar, Sonar and Navigation, Volume: 152, Issue: 4, 2005 2. Borre, K., D. Akos, N. Bertelsen, P. Rinder, and S. Jensen, A Software-Defined GPS and Galileo Receiver: A Single-Frequency Approach, Applied and Numerical Harmonic Analysis, Birkhäuser, 2007 3. Fernández–Prades, C., J. Arribas, P. Closas, C. Avilés, and L. Esteve, “GNSS-SDR: An Open Source Tool For Researchers and Developers,” Proceedings of the ION GNSS 2011 Conference, Portland, OR, 2011 4. Fernández–Prades, C., J. Arribas, L. Esteve, D. Pubill, and P. Closas, “An Open Source Galileo E1 Software Receiver,” Proceedings of the 6th ESA Workshop on Satellite Navigation Technologies (NAVITEC ’12), ESTEC, Noordwijk, The Netherlands, 2012 5. Fonzo, A. D., M. Leonardi, G. Galati, P. Madonna, and L. Sfarzo, “Software-Defined-Radio Techniques Against Jammers for In-Car GNSS Navigation,” 2014 IEEE Metrology for Aerospace (MetroAeroSpace), 2014 6. Goff, S., “Reports of Mass GPS Spoofing Attack in the Black Sea Strengthen Calls for PNT Backup,” Inside GNSS, 2017 7. Kraus, T., R. Bauernfeind, and B. Eissfeller, “Survey of In-Car Jammers – Analysis and Modeling of the RF Signals and IF Samples (Suitable for Active Signal Cancelation),” Proceedings of the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2011), Portland, OR, 2011 8. Pany, T., Navigation Signal Processing for GNSS Software Receivers, GNSS Technology and Applications Series, Artech House, 2010 9. Pany, T., N. Falk, B. Riedl, T. Hartmann, G. Stangl, and C. Stöber, “An Answer for Precise Positioning Research,” Innovation: Software GNSS Receiver, 2012 10. Petrovski, I., and T. Tsujii, Digital Satellite Navigation and Geophysics: A Practical Guide with GNSS Signal Simulator and Receiver Laboratory, Digital Satellite Navigation and Geophysics, Cambridge University Press, 2012 11. Revnivykh, I., “Glonass Programme Update,” 11th Meeting of the International Committee on Global Navigation Satellite Systems, Sochi, Russian Federation, 2016 12. Russian Institute of Space Device Engineering, “Global Navigation Satellite System (GLONASS) Interface Control Document, Navigational Radio Signal in Bands L1, L2,” Technical Report, Moscow, 2008 13. Shivaramaiah, N. C., D. M. Akos, and K. Yan, “A Multi-band GNSS Signal Sampler Module with Open-Source Software Receiver,” Proceedings of the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2016), Portland,

OR, 2016 14. Siouris, G. M., Aerospace Avionics Systems : A Modern Synthesis, Academic Press, 1993 15. Suzuki, T. and N. Kubo, “GNSS-SDRLIB: An OpenSource and Real-Time GNSS Software Defined Radio Library,” Proceedings of the 27th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2014), Tampa, FL, 2014 16. Takasu, T. and A. Yasuda, “RTKLIB ver. 2.4.2 Manual,” No. C, 2013

Authors

Damian Miralles is a graduate student in the Department of Aerospace Engineering Sciences at the University of Colorado Boulder. He received a B.S. in Electrical and Computer Engineering from the Polytechnic University of Puerto Rico. His research interests are in GNSS receiver technologies, software defined radio and digital signal processing.

Gabriel F. P. Araujo is an undergraduate student in the Faculty of Technology at the University of Brasilia, Brazil, and scholarship researcher at LARA (Automation and Robotics Laboratory). He works with robotics estimation and navigation. He is currently working with SDR development, a software-defined radio for mobile robot localization using multi-constellation GNSS systems.

Em. Univ.-Prof. Dr.-Ing. habil. Dr. h.c. Guenter W. Hein is Professor Emeritus of Excellence at the University FAF Munich. He was ESA Head of EGNOS & GNSS Evolution Programme Dept. between 2008 and 2014, in charge of development of the 2nd generation of EGNOS and Galileo. Prof. Hein is still organizing the ESA/JRC International Summerschool on GNSS. He is the founder of the annual Munich Satellite Navigation Summit. Prof. Hein has more than 300 scientific and technical papers published, carried out more than 200 research projects and educated more than 70 Ph. D.´s. He received in 2002 the prestigious Johannes Kepler Award for “sustained and significant contributions to satellite navigation” of the US Institute of Navigation, the highest worldwide award in navigation given only to one individual each year. G. Hein became a Fellow of ION in 2011. The Technical University of Prague honored his achievements in satellite navigation with a Doctor honoris causa in Jan. 2013. He is a member of the Executive Board of Munich Aerospace since 2016.

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