Skydel, a GNSS test solutions company, has partnered with Germany-based Noffz to deliver SDX GNSS simulation to clients in Europe.
Noffz creates test systems and solutions in the area of the Internet of Things (IoT) — especially in automotive RF-test applications around eCall, network access devices, telematics control units, infotainment/multimedia units and automotive radar.
With nearly 30 years of experience, Noffz delivers worldwide turnkey solutions and PC-based measurement, as well as automation systems.
“With their broad expertise in test solutions, Noffz is well positioned to bring Skydel’s SDX GNSS simulation solutions to clients located in Europe and beyond,” Skydel said in a blog.
“Technology is constantly evolving,” reads the blog. “With the advent of new satellite constellations, such as Galileo, expanding needs for position and navigation in the transportation industry, and the growing threats of RF interferences, GNSS simulation is more than ever a key component in the arsenal needed to design and validate new products.
“Skydel SDX delivers a new paradigm in GNSS simulation, featuring an exclusive mix of performance, flexibility and unique capabilities. With the addition of Noffz’s know-how covering multiple industries, we now have an outstanding team that’s ready to tackle today and tomorrow’s technological integration challenges.”
Galileot will reach Full Operational Capability (FOC) in 2019. Simulation of the complete Galileo constellation is possible with Skydel’s SDX GNSS simulator.
Sensor role reversal: Lidar with its superior performance can replace GNSS in the integration solution by providing fixes for the drifting inertial measurement unit (IMU). Tests show its potential for terrain-referenced navigation due to its high accuracy, resolution, update rate and anti-jamming abilities. A novel algorithm uses scanning lidar ranging data and a reference database to calculate the navigation solution of the platform and then further fuse with the inertial navigation system (INS) output data.
Recent rapid advances in laser-based remote sensing technologies, including pulsed linear, array and flash lidar systems, have fostered the development of integrated navigation algorithms for lidar and inertial sensors. In particular, trajectory recovery based on lidar point-cloud matching can provide valuable input to the navigation filter. Lidar/INS integrated navigation systems may provide continuous and fairly accurate navigation solutions in GNSS-challenged environments, on a variety of platforms, such as unmanned ground vehicles, mobile robot navigation and autonomous driving.
In the case of airborne lidar/INS applications, the free inertial navigation solution is used to create the point clouds, which are subsequently matched to a digital terrain elevation model (DEM). The results are fed back to the platform navigation filter, providing corrections to the free navigation solution. This solution may be used to recreate the point cloud to obtain better surface data.
However, depending on the lidar data acquisition parameters, INS drift during the time between the two epochs when point clouds are acquired could be significant. Besides the shift in platform position, the drift in attitude angles could more severely impact point-cloud generation, producing a less accurate point cloud and subsequently poor matching performance.
This article describes a new lidar positioning approach, where the scale-invariant feature transform (SIFT)-based lidar positioning algorithm is used to match between the lidar measured point cloud and the reference DEM. The matching process is aided with fuzzy control: SIFT-based lidar positioning algorithm with Fuzzy logic (SLPF), where the threshold for SIFT is adaptively controlled by the fuzzy logic system.
Based on the geometric distribution and the range difference variance of the matched point clouds, fuzzy logic is applied to calculate the threshold for the SIFT algorithm to extract feature points; thus the optimal matched point cloud is extracted in several iterations. When there are enough matched points in the final output of the SLPF, the platform position is calculated by using the least squares method (LSM). Next, for trajectory estimation, when applying the SLPF algorithm, frequent lidar updates can be used to correct small cumulative errors from the INS sensor measurements. A Kalman filter fuses the results of the SLPF algorithm with the INS system.
This integrated algorithm can handle situations when there are less than three matched feature points being extracted by the SLPF algorithm, and yet they could still contribute to obtain a better navigation solution. Simulation results show that, compared to the existing algorithms, the proposed lidar/INS integrated navigation algorithm not only improves the position, speed and attitude-determination accuracy, it also makes the lidar less dependent on INS, which makes the navigation system work longer without exceeding a particular drift threshold.
LIDAR ALGORITHM
To eliminate the influence of INS error on the lidar positioning system, instead of creating a measured DEM based on INS ortho-rectification, we directly map the range data measured by lidar to the local stored DEM data. If a successfully matched feature point can be obtained, it means that we can get a point with absolute position and relative range towards the platform, which is similar to the satellite in GNSS positioning. After scanning of one area by lidar, when three or more such matched feature points, if not on a line, can be obtained, then we are able to form a full rank equation with the unknown variables of the platform position x, y and z.
However, due to the effect of affine transformation, the standardized range dataset collected by lidar is significantly different from the elevation dataset belonging to the same area. Figure 1 shows an example of the large difference between the two datasets from the same area when the pitch angle of the platform is equal to 5° and the flying height is 2,000 m. In this situation, the traditional flooding algorithm or constellation feature point matching algorithm is incapable of extracting matched feature points from such different datasets.
Figure 1. Comparison between SR and DEM data from the same area.
In response, we introduce the SIFT algorithm to the elevation map-matching procedure. Designed for image matching, the SIFT algorithm is invariant to scale, rotation and translation, and it is robust to affine transformation and three-dimensional projection transformation to a certain extent. Although SIFT is often used in image matching, each pixel from the image is a numerical point, which, in fact, has no difference with elevation data point. Before applying the SIFT, some processing on the lidar measured range data must be done.
LIDAR RANGE DATA
The scanning information of the lidar measured points are (α, β, r), where α is the angle between the laser beam and the negative Z-axis of the platform body frame, β is the angle from the laser beam to the plane of axis and Z-axis in body frame, r is the range between the laser head and the measured target, as shown in the opening figure.
Due to the terrain relief, the lidar range data are irregularly spaced. Therefore, it is necessary to interpolate the collected data. Here we apply the Natural Neighbor Interpolation method.
SIFT Algorithm, Fuzzy Control. For the lidar positioning algorithm, which is based on the absolute position and relative range of the ground-matched feature points, a point cloud with sufficient number of points of good geometric distribution is needed. In practice, however, the terrain undulation and the attitude of the airplane will affect the quality of the point cloud and the accuracy in the matching process. In addition, the selected threshold in the SIFT algorithm plays an important role on the quality of the matched point cloud.
A Monte Carlo simulation, shown in FIGURE 2, illustrates the impact of the threshold on the number of successful matched points (normalized) and mismatched rate. For obtaining better matched point clouds, we have introduced a SIFT terrain matching algorithm assisted by fuzzy control, as shown in FIGURE 3.
Figure 2. Relationship effect of threshold on the number of successful matched point (normalized) and error matched rate.Figure 3. Working principal diagram of SIFT terrain matching algorithm based on fuzzy control.
The algorithm mainly consists of two fuzzy logic controllers. Controller 1 calculates the initial threshold for the SIFT algorithm according to the gridded SR data terrain undulation degree λ, and the angle Θ between Z-axis in body-frame and Z-axis in navigation frame.
Controller 2, which is responsible to adaptively changing the threshold at each epoch, has two inputs. The first one is the Normalized Points Area (NPA), which represent the geometric condition of the matched point cloud. The other one is the Relative Range Difference Variance, which indicates if a mismatch has happened. When the final matched feature point cloud is obtained, and the number of points is greater than or equal to 3, then the LSM is used to calculate the position of the platform.
INS/LIDAR NAVIGATION
Loosely and tightly coupled integration are the most common methods in navigation systems. Given the characteristics of the proposed positioning algorithm, the classical integrated navigation algorithm needs to be modified. In the loosely coupled approach, the lidar is unable to aid INS when flying through a flat region and/or flying with a large tilt angle, because the proposed lidar positioning method may have difficulty in extracting enough matched points to calculate a position.
In the tightly coupled method, as the output frequency of matched point cloud is low and the geometry of the matched feature points is relatively poor, the integrated system may be extremely unstable. Here we propose a combined loosely and tightly (CLT) integrated navigation algorithm that when the lidar positioning algorithm can extract enough matched points for a navigation solution, the lidar-calculated navigation solution is used as the main observation.
However, when the matched points are not sufficient to obtain a navigation solution, the baseline vector of the matched point that is closer to the projection of the platform center to the surface will be utilized as the observation. In this solution, lidar can still provide a certain degree of aid to the INS, once extracting matched feature points, even if less than 3.
SIMULATION ANALYSIS
In the simulation experiment, the 3D DEM data of 0.5-meter resolution is obtained from an open source named EOWEB. Then the DEM data is resampled to a higher resolution of 0.1 meter, which is used to generate the simulated, irregularly spaced, measured range data. On the basis of the original DEM (0.5 meter resolution), the proposed lidar positioning algorithm and lidar/INS integrated navigation algorithm are verified and compared with the traditional methods.
Simulation of Lidar Algorithm. As shown above, the successfully matched points rate is very important for positioning, as once a mismatched point occurs, it may lead to a faulty navigation solution. In the simulation, the proposed SLPF is simulated under the condition of different aircraft tilt angle ϴ, from 0° to 10° with a step of 1° , at 5,000 different positions, which is the same simulation condition as in Figure 2. Comparison is made with the traditional constellation feature matching based lidar positioning algorithm (CLP) and the SIFT based lidar positioning algorithm without fuzzy control (SLP). The successfully matched points rate and the NPA value are shown in Figure 4.
Figure 4. Successful points matched rate and the NPA value results under different aircraft attitude condition from three different algorithms.
As can be seen from the figure, along with the increasing platform attitude angle, the successfully matched points rate of all the three algorithms has declined. However, compared to the CLP, both SIFT-based algorithms have a higher success matching rate due to the more stringent feature-point extraction approach. And due to the adjustable threshold mechanism, the SLPF could remove some of the mismatched points by raising the threshold; thus it is superior to the common SIFT algorithm in performance. The NPA values of the extracted point cloud from the three algorithms are shown in Figure 4(b). With the increased attitude angle, the NPA value of the matching feature point cloud decreases in all three algorithms. The CLP algorithm, however, is more sensitive to the projected range data, which makes the number of successful matching points drop sharply, and further affect geometric distribution of the point cloud. The gap between the SLPF and SLP shows that the fuzzy control module can help improve the geometric structure of the feature point cloud.
Figure 5 shows the positioning error when applying the three different matching algorithms at 5,000 different areas. The SLPF algorithm is better than the other two algorithms in all directions. When the platform’s attitude angle reaches about 10 degrees, the north and east positioning accuracy of SLPF algorithm is still about 8 meters, and the height positioning accuracy is about 0.2 meters. The reason that the height positioning error is far less than the north and east positioning error is because of the matching point cloud distribution. Due to the airborne lidar scanning mechanism, the matched point cloud is all located in a relative small area at the bottom of the platform, resulting in the great component value in the height direction of each matched feature point baseline vector in the G matrix, and then affect the final positioning accuracy.
Figure 5. Positioning accuracy under different aircraft attitude conditions with different algorithms.
Table 1 shows some detailed information as average number of matched points (ANMP) and matched points position error (MPPE) using the three methods. The MPPE is calculated in 3D space. It can be seen that when the tilt attitude is small, comparing to the CLP method, although the number of matched points extracted by SLPF is less, the matched points position accuracy is still much better, leading to a better localization result. Moreover, with the increasing platform tilt attitude, CLP and SLP have more difficulty in maintaining the number and accuracy of the matched points.
Lidar/INS Algorithm. To validate the feasibility of the proposed integrated navigation algorithm, firstly, the motion trajectory of the platform must be simulated. As shown in Figure 6, the red line is the simulated platform true trajectory, which lasts for 1,400 seconds. During the trajectory, the platform undertakes the different motion states as acceleration, deceleration, climbing, turning and descent. Then the INS output data based on the true trajectory with the frequency of 100 Hz is generated. To verify the calibration performance on the INS in the integrated navigation algorithm, accelerometer and gyroscope drift noise is added to the INS output data. The green line shown in Figure 6 is the INS output data trajectory solution. At the end of simulation, the error to the east direction reaches 500 meters, and the north direction error reaches to more than 2,200 meters.
Figure 6. Comparison between True trajectory and INS calculated trajectory.
At the same time of the INS outputting navigation solution, lidar also scans and calculates the position of the platform with 1-Hz frequency. Note that the speed of the aircraft is from 70 m/s to 100 m/s, and the maximum lidar scanning angle αmax is 20°. Figure 7 and Figure 8 show the number of matched points and the positioning error for each scanned terrain using SLFP. When the platform maintains smooth flying, the number of matched points can reach an average of 10, and the positioning accuracy is relatively high, less than 3 meters. Note, during the period, only in a few epochs are the number of matched points less than five. However, when the platform is climbing or changing flight direction, the number of matched points is obviously decreased due to the large tilt angle of the platform, and so does the number of successful positioning times. In this case, the position error is also increased dramatically, reaching about 10 meters error in east and north, and 0.2 meters error in height. Especially in the course of changing the direction of the flight, shown in Figure 7, during the periods of 720s–800s and 920s–1,000s, due to the larger roll angle, the SLPF could hardly be able to calculate the position through the LSM. During this period the lidar would occasionally output 1 or 2 matched feature points.
Figure 7. The number of the matched points of each lidar positioning epoch.
Figure 8. The positioning accuracy of each lidar positioning epoch.
During the simulation, the CLT and LC methods are used for data fusion and trajectory estimation comparisons. TC method is not added to the comparison because of slow convergence. The data fusion results are shown in Figure 9. It illustrates that the LC method and the CLT method have close positioning accuracy in the case of sufficient matched feature points. As can be seen in conjunction with Figure 8, when lacking matched points, the CLT method is superior to LC on positioning accuracy, especially in the height direction. In addition, the CLT integrated algorithm shows some improvement on the accuracy of estimating speed and attitude.
Figure 9a. Data fusion results using two different integrated algorithms: position determination error.
Figure 9b. Data fusion results using two different integrated algorithms:velocity determination error.
Figure 9c. Data fusion results using two different integrated algorithms: attitude determination error.
Figure 10 shows the position error distribution when using four different lidar/INS integrated navigation methods for data fusion under the condition of different simulation trajectories. In the simulation, 50 1,400-second-long different trajectories, with flat areas, are generated with different platform attitude, velocity or acceleration. As can be seen from the figure, compared to other integrated navigation methods, the CLT method greatly improves the accuracy of navigation.
Figure 10. Position error distribution when using four different lidar/INS integrated navigation method.
During 84.26% of the simulation period, CLT could maintain the position error less than 3 meters; the rate with error that is larger than 15 meters is 1.2%. For the TC method, due to the frequent divergence of the data fusion filter, most of the position estimates are not available. In addition, after flying above a flat area, the voting-based constellation integrated method has poor matched point accuracy and successfully matched rate due to large INS drift error, which makes lidar unable to calibrate the INS. When using the constellation-based method, during only 32.35% of the simulation period, the error is maintained in 3 meters and most of the period, 54.9%, the position error is between 3 to 15 meters.
CONCLUSION
We propose a new lidar matching algorithm based on SIFT, which does not rely on the INS output data to generate measured DEM data, and can adaptively change the threshold of the SIFT algorithm to generate optimal matching between the point cloud and the DEM. Through verification of simulation, the algorithm is compared with traditional lidar/INS integrated navigation methods based on comparing achieved accuracies in estimating position, speed and attitude. Simulation results show that the SLPF algorithm has better reliability for feature points matching and robustness against the platform attitude than the traditional algorithms. The CLT method improves trajectory estimation accuracy, especially when flying over moderately undulating terrain or flying with large roll or pitch angles.
ACKNOWLEDGMENT
This article is based on a paper presented at the ION International Technical Meeting, January 2017. This research used an open-source GNSS/INS simulator based on Matlab, developed by Gongmin Yan of Northwestern Polytechnical University, China.
Haowei Xu is a Ph.D. student at Northwestern Polytechnical University, where he received an M.Sc in Information and Communication Engineering. He is a visiting scholar at The Ohio State University.
Baowang Lian is a professor at Northwestern Polytechnical University where he is also director of the Texas Instruments DSPs Laboratory.
Charles K. Toth is a senior research scientist at the Ohio State University Center for Mapping. He received a Ph.D. in electrical engineering and geo-information sciences from the Technical University of Budapest, Hungary.
Dorota A. Brzezinska is a professor in geodetic science, and director of the Satellite Positioning and Inertial Navigation (SPIN) Laboratory at The Ohio State University.
New cars for the Russian market must be equipped with the automatic ERA-GLONASS emergency call system.
For certification of these in-vehicle systems, both conformance and performance tests are mandatory, in line with the Russian GOST R 55534 specification.
The Rohde & Schwarz CMW500 is being used to test the ERA-GLONASS system.
For both types of tests, the Russian Certification Center Svyaz-Certificate uses standard-compliant test solutions from Rohde & Schwarz. Manufacturers and component suppliers can use the same test solution in pre-tests to speed up certification for their products.
Now, for the newly required performance test, the center is using the GNSS simulator in the R&S SMBV100A vector signal generator.
Accuracy Requirements. During performance testing, it is verified whether the GNSS receiver of an ERA-GLONASS emergency call system fulfills the accuracy requirements of the specification.
In case of an emergency, the call system should not only correctly transmit position data according to a specified protocol to the public safety answering point, but position data must also be accurate so that the first responder can locate the accident vehicle quickly.
ERA-GLONASS module manufacturers and test houses can use the R&S SMBV100A during pre-tests to create reliable and reproducible conditions similar to those in official certification tests, according to Rohde & Schwarz, to minimize the risk of failing tests during certification.
Talen-X has been given security approval by the GPS Directorate, allowing BroadSim to create and process Y-Code while in a classified environment.
BroadSim is a software-defined GNSS simulator that enables users to easily model true and spoofed signals. BroadSim was developed to simplify advanced jamming and spoofing scenarios with Navigation Warfare (NAVWAR) testing in mind.
BroadSim supports high dynamics, advanced jamming, spoofing and encrypted military codes.
Powered by Skydel’s SDX 1000-Hz software simulator engine, BroadSim can simulate multiple constellations including GPS, GLONASS, Galileo and BeiDou.
Software features:
Capable of generating and simulating multiple signal types
GPS L1, L2 with C/A, P, Y and M
GLONASS G1 and G2
Galileo E1 and E5
BeiDou B1 and B2
Intuitive control using Skydel’s SDX software
Utilizes four RF outputs, each with multiple simultaneous constellations
Generates high-fidelity jamming and interference signals
BroadSim hardware includes a generator and controller with two integrated commercial-off-the-shelf USRP radios, an integrated OctoClock-G with GPS disciplined oscillator, four frequency-independent transmit and receive channels and a UBX-160 RF daughterboard.
Averna and Skydel Solutions will be showcasing their latest GNSS technology simulation products at The Institute of Navigation (ION) GNSS+ conference, giving show-goers the opportunity to see the RP-6100 in action.
ION GNSS+, taking place Sept. 14-18 in Tampa, Fla., is the 28th International Technical Meeting of the Institute of Navigation’s Satellite Division and the world’s largest technical meeting and showcase of GNSS and related technology, products and services.
The companies will be presenting at Booth #100 in the Exhibit Hall, meeting attendees and discussing their latest innovations in GNSS receiver validation, among other topics. They will also be demonstrating two new GNSS solutions:
RP-6100 Multi-Channel RF Record & Playback: The RP-100 for RF application testing allows users to tecord real-world signals such as GNSS, HD Radio, LTE and Wi-Fi — plus impairments — to significantly advance projects and harden product designs.
GNSS Simulator: New from Averna’s partner Skydel Solutions, this innovative and cost-effective simulator is entirely software driven. It’s a perfect fit with the RP-6100, Averna said, enabling users to test corner cases and future events with a real-time GNSS solution.
Averna RP-6100 record and playback solution. (PRNewsFoto/Averna)
In recent years, the sporting world has seen an explosion in the use of GPS. You will rarely spot a runner or cyclist on the road without either a smartphone strapped to their arm or a dedicated GPS device clamped to their handlebars, tracking their every move.
The amount of information that the modern sportsperson — from casual amateur to full-time professional — logs, analyzes, and shares is phenomenal. There are now dozens of ways of uploading data for the whole world to share and study.
As more manufacturers come to this market with the hope of capturing a share of it, they face the challenge of effectively developing and then testing their devices. Among many factors to consider, new products must have capability for local constellations such as BeiDou, GLONASS, and QZSS, not just GPS alone. New market entrants won’t have the same budget as the established big players, and constantly traveling to China or Japan to try out a new gadget will escalate costs to an unsustainable degree.
Then there’s the issue of getting out into the kind of environment in which you imagine your new sporting GPS device will be put to use. In many cases this will be remote: forests, hills, and mountains. Stepping outside to the office car park does not constitute a sufficient test for satellite acquisition and retention. Neither does simply driving the commute route home with it.
A GPS simulator or replay device allows for bench testing, but such devices are expensive. They might not actually fulfill your testing requirements, either: a traditional GPS simulator outputs its scenarios based on constellation modeling, either as a perfect signal or one that has simulated multipath. But you need to genuinely know how your new product will operate through, say, a forest on a downhill mountain bike run, or during a city marathon through urban canyons, or on a trail under wet trees. Adventure sport participants want to record their achievements wherever they go.
How do you obtain this kind of realistic scenario? It will require the use of a GNSS recorder, and in an ideal world you would lend it to someone who actually does some of this stuff. Perhaps one of your colleagues is an (insane) downhill skier — who better to capture exactly that type of data, which you can replay back in a nice warm lab?
The trouble is that a person of this sporting ilk will be unwilling or unable to carry bulky equipment that weighs several kilos. It will slow them down, so a GNSS recorder that can be easily carried without affecting the sporting activity is essential. It has to be easy to use: self-contained, with a battery that will last a couple of hours, and with one big button to start and stop recording. The user shouldn’t need any training in its operation. And ideally, it won’t need a large ground-plane antenna to capture usable data; a well-designed unit will employ a sensitive GPS engine allowing for as complete a signal as possible to be logged through a standard passive antenna.
Looking further afield, other industries will soon be seeking a device with this level of convenience. For instance, agricultural and automotive manufacturers want the ability to send test engineers out to record drive-cycle tests easily and in a variety of vehicles. Additional features, such as controlled area network (CAN) and inertial sensor logging, synchronized with the GNSS data, will also find favor.
The nature of the simulation market is changing: increasing numbers of developers need not just a traditional constellation simulator, but rather a replay device that is feature-rich and that doesn’t cost the earth.
Economies of scale will likely dictate the way that this develops, and GNSS simulation will no longer be the specialist and exclusive field it once was.
Mark Sampson is the LabSat product manager for RaceLogic, based in Buckingham, UK.
Generating Distorted GNSS Signals Using a Signal Simulator
By Mathieu Raimondi, Eric Sénant, Charles Fernet, Raphaël Pons, Hanaa Al Bitar, Francisco Amarillo Fernández, and Marc Weyer
INNOVATION INSIGHTS by Richard Langley
INTEGRITY. It is one of the most desirable personality traits. It is the characteristic of truth and fair dealing, of honesty and sincerity. The word also can be applied to systems and actions with a meaning of soundness or being whole or undivided. This latter definition is clear when we consider that the word integrity comes from the Latin word integer, meaning untouched, intact, entire — the same origin as that for the integers in mathematics: whole numbers without a fractional or decimal component.
Integrity is perhaps the most important requirement of any navigation system (along with accuracy, availability, and continuity). It characterizes a system’s ability to provide a timely warning when it fails to meet its stated accuracy. If it does not, we have an integrity failure and the possibility of conveying hazardously misleading information. GPS has built into it various checks and balances to ensure a fairly high level of integrity. However, GPS integrity failures have occasionally occurred.
One of these was in 1990 when SVN19, a GPS Block II satellite operating as PRN19, suffered a hardware chain failure, which caused it to transmit an anomalous waveform. There was carrier leakage on the L1 signal spectrum. Receivers continued to acquire and process the SVN19 signals, oblivious to the fact that the signal distortion resulted in position errors of three to eight meters. Errors of this magnitude would normally go unnoticed by most users, and the significance of the failure wasn’t clear until March 1993 during some field tests of differential navigation for aided landings being conducted by the Federal Aviation Administration. The anomaly became known as the “evil waveform.”
(I’m not sure who first came up with this moniker for the anomaly. Perhaps it was the folks at Stanford University who have worked closely with the FAA in its aircraft navigation research. The term has even made it into popular culture. The Japanese drone-metal rock band, Boris, released an album in 2005 titled Dronevil. One of the cuts on the album is “Evil Wave Form.” And if drone metal is not your cup of tea, you will find the title quite appropriate.) Other types of GPS evil waveforms are possible, and there is the potential for such waveforms to also occur in the signals of other global navigation satellite systems. It is important to fully understand the implications of these potential signal anomalies. In this month’s column, our authors discuss a set of GPS and Galileo evil-waveform experiments they have carried out with an advanced GNSS RF signal simulator. Their results will help to benchmark the effects of distorted signals and perhaps lead to improvements in GNSS signal integrity.
“Innovation” is a regular feature that discusses advances in GPS technology andits applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering, University of New Brunswick. He welcomes comments and topic ideas.
GNSS signal integrity is a high priority for safety applications. Being able to position oneself is useful only if this position is delivered with a maximum level of confidence. In 1993, a distortion on the signals of GPS satellite SVN19/PRN19, referred to as an “evil waveform,” was observed. This signal distortion induced positioning errors of several meters, hence questioning GPS signal integrity. Such events, when they occur, should be accounted for or, at least, detected.
Since then, the observed distortions have been modeled for GPS signals, and their theoretical effects on positioning performance have been studied through simulations. More recently, the models have been extended to modernized GNSS signals, and their impact on the correlation functions and the range measurements have been studied using numerical simulations. This article shows, for the first time, the impact of such distortions on modernized GNSS signals, and more particularly on those of Galileo, through the use of RF simulations. Our multi-constellation simulator, Navys, was used for all of the simulations.
These simulations are mainly based on two types of scenarios: a first scenario, referred to as a static scenario, where Navys is configured to generate two signals (GPS L1C/A or Galileo E1) using two separate RF channels. One of these signals is fault free and used as the reference signal, and the other is affected by either an A- or B-type evil waveform (EW) distortion (these two types are described in a latter section).
The second type of scenario, referred to as a dynamic scenario, uses only one RF channel. The generated signal is fault free in the first part of the simulation, and affected by either an A- or B-type EW distortion in the second part of the scenario. Each part of the scenario lasts approximately one minute.
All of the studied scenarios consider a stationary satellite position over time, hence a constant signal amplitude and propagation delay for the duration of the complete scenario.
Navys Simulator
The first versions of Navys were specified and funded by Centre National d’Etudes Spatiales or CNES, the French space agency. The latest evolutions were funded by the European Space Agency and Thales Alenia Space France (TAS-F). Today, Navys is a product whose specifications and ownership are controled by TAS-F. It is made up of two components: the hardware part, developed by ELTA, Toulouse, driven by a software part, developed by TAS-F.
The Navys simulator can be configured to simulate GNSS constellations, but also propagation channel effects. The latter include relative emitter-receiver dynamics, the Sagnac effect, multipath, and troposphere and ionosphere effects. Both ground- and space-based receivers may be considered.
GNSS Signal Generation Capabilities. Navys is a multi-constellation simulator capable of generating all existing and upcoming GNSS signals. Up to now, its GPS and Galileo signal-generation capabilities and performances have been experienced and demonstrated. The simulator, which has a generation capacity of 16 different signals at the same time over the entire L band, has already been successfully tested with GPS L1 C/A, L1C, L5, and Galileo E1 and E5 receivers.
Evil Waveform Emulation Capabilities. In the frame of the ESA Integrity Determination Unit project, Navys has been upgraded to be capable of generating the signal distortions that were observed in 1993 on the signals from GPS satellite SVN19/PRN19. Two models have been developed from the observations of the distorted signals.
The first one, referred to as Evil Waveform type A (EWFA), is associated with a digital distortion, which modifies the duration of the GPS C/A code chips, as shown in FIGURE 1. A lead/lag of the pseudorandom noise code chips is introduced. The +1 and –1 state durations are no longer equal, and the result is a distortion of the correlation function, inducing a bias in the pseudorange measurement equal to half the difference in the durations. This model, based on GPS L1 C/A-code observations, has been extended to modernized GNSS signals, such as those of Galileo (see Further Reading). In Navys, type A EWF generation is applied by introducing an asymmetry in the code chip durations, whether the signal is modulated by binary phase shift keying (BPSK), binary offset carrier (BOC), or composite BOC (CBOC).
FIGURE 1. Theoretical L1 C/A code-chip waveforms in the presence of an EWFA (top) and EWFB (bottom).
The second model, referred to as Evil Waveform type B (EWFB) is associated with an analog distortion equivalent to a second-order filter, described by a resonance frequency (fd) and a damping factor (σ), as depicted in Figure 1. This failure results in correlation function distortions different from those induced by EWFA, but which also induces a bias in the pseudorange measurement. This bias depends upon the characteristics (resonance frequency, damping factor) of the filter. In Navys, an infinite impulse response (IIR) filter is implemented to simulate the EWFB threat. The filter has six coefficients (three in the numerator and three in the denominator of its transfer function). Hence, it appears that Navys can generate third order EWF type B threats, which is one order higher that the second order threats considered by the civil aviation community. Navys is specified to generate type B EWF with less than 5 percent root-mean-square (RMS) error between the EWF module output and the theoretical model. During validation activities, a typical value of 2 percent RMS error was measured. This EWF simulation function is totally independent of the generated GNSS signals, and can be applied to any of them, whatever its carrier frequency or modulation.
It is important to note that such signal distortions may be generated on the fly — that is, while a scenario is running. FIGURE 2 gives an example of the application of such threat models on the Galileo E1 BOC signal using a Matlab theoretical model.
FIGURE 2. Theoretical E1 C code-chip waveforms in the presence of an EWFA (top) and EWFB (bottom).
GEMS Description
GEMS stands for GNSS Environment Monitoring Station. It is a software-based solution developed by Thales Alenia Space aiming at assessing the quality of GNSS measurements. GEMS is composed of a signal processing module featuring error identification and characterization functions, called GEA, as well as a complete graphical user interface (see online version of this article for an example screenshot) and database management.
The GEA module embeds the entire signal processing function suite required to build all the GNSS observables often used for signal quality monitoring (SQM). The GEA module is a set of C/C++ software routines based on innovative-graphics-processing-unit (GPU) parallel computing, allowing the processing of a large quantity of data very quickly. It can operate seamlessly on a desktop or a laptop computer while adjusting its processing capabilities to the processing power made available by the platform on which it is installed. The GEA signal-processing module is multi-channel, multi-constellation, and supports both real-time- and post-processing of GNSS samples produced by an RF front end.
GEMS, which is compatible with many RF front ends, was used with a commercial GNSS data-acquisition system. The equipment was configured to acquire GNSS signals at the L1 frequency, with a sampling rate of 25 MHz. The digitized signals were provided in real time to GEMS using a USB link.
From the acquired samples, GEMS performed signal acquisition and tracking, autocorrelation function (ACF) calculation and display, and C/N0 measurements. All these figures of merit were then logged in text files.
EWF Observation
Several experiments were carried out using both static and kinematic scenarios with GPS and Galileo signals.
GPS L1 C/A. The first experiment was intended to validate Navys’ capability of generating state-of-the-art EWFs on GPS L1 C/A signals. It aimed at verifying that the distortion models largely characterized in the literature for the GPS L1 C/A are correctly emulated by Navys.
EWFA, static scenario. In this scenario, Navys is configured to generate two GPS L1 C/A signals using two separate RF channels. The same PRN code was used on both channels, and a numerical frequency transposition was carried out to translate the signals to baseband. One signal was affected by a type A EWF, with a lag of 171 nanoseconds, and the other one was EWF free. Next, its amplified output was plugged into an oscilloscope. The EWFA effect is easily seen as the faulty signal falling edge occurs later than the EWF-free signal, while their rising edges are still synchronous. However, the PRN code chips are distorted from their theoretical versions as the Navys integrates a second-order high pass filter at its output, meant to avoid unwanted DC emissions. The faulty signal falling edge should occur approximately 0.17 microseconds later than the EWF-free signal falling edge.
A spectrum analyzer was used to verify, from a spectral point of view, that the EWFA generation feature of Navys was correct. For this experiment, Navys was configured to generate a GPS L1 C/A signal at the L1 frequency, and Navys output was plugged into the spectrum analyzer input. Three different GPS L1 C/A signals are included: the spectrum of an EWF-free signal, the spectrum of a signal affected by an EWF type A, where the lag is set to 41.1 nanoseconds, and the spectrum of a signal affected by an EWF type A, where the lag is set to 171 nanoseconds. As expected, the initial BPSK(1) signal is distorted and spikes appear every 1 MHz. The spike amplitude increases with the lag.
EWFA, dynamic scenario. In a second experiment, Navys was configured to generate only one fault-free GPS L1 C/A signal at RF. The RF output was plugged into the GEMS RF front end, and acquisition was launched. One minute later, an EWFA distortion, with a lag of 21 samples (about 171 nanoseconds at 120 times f0, where f0 equals 1.023 MHz), was activated from the Navys interface.
FIGURE 3 shows the code-phase measurement made by GEMS. Although the scenario was static in terms of propagation delay, the code-phase measurement linearly decreases over time. This is because the Navys and GEMS clocks are independent and are drifting with respect to each other.
The second observation is that the introduction of the EWFA induced, as expected, a bias in the measurement. If one removes the clock drifts, the bias is estimated to be 0.085 chips (approximately 25 meters). According to theory, an EWFA induces a bias equal to half the lead or lag value. A value of 171 nanoseconds is equivalent to about 50 meters.
FIGURE 4 represents the ACFs computed by GEMS during the scenario. It appears that when the EWFA is enabled, the autocorrelation function is flattened at its top, which is typical of EWFA distortions. Eventually, FIGURE 5 showed that the EWFA also results in a decrease of the measured C/N0, which is completely coherent with the flattened correlation function obtained when EWFA is on.
Additional analysis has been conducted with Matlab to confirm Navys’ capacity. A GPS signal acquisition and tracking routine was modified to perform coherent accumulation of GPS signals. This operation is meant to extract the signal out of the noise, and to enable observation of the code chips. After Doppler and code-phase estimation, the signal is post-processed and 1,000 signal periods are accumulated. The result, shown in FIGURE 6, confronts fault-free (blue) and EWFA-affected (red) code chips. Again, the lag of 171 nanoseconds is clearly observed. The analysis concludes with FIGURE 7, which shows the fault-free (blue) and the faulty (red) signal spectra. Again, the presence of spikes in the faulty spectrum is characteristic of EWFA.
FIGURE 6. Fault-free vs. EWFA GPS L1 C/A signal.FIGURE 7. Fault-free vs. EWFA GPS L1 C/A signal power spectrum density.
EWFB, static scenario. The same experiments as for EWFA were conducted for EWFB. Fault-free and faulty (EWFB with a resonance frequency of 8 MHz and a damping factor of 7 MHz) signals were simultaneously generated and observed using an oscilloscope and a spectrum analyzer. The baseband temporal signal undergoes the same default as that of the EWFA because of the Navys high-pass filter. However, the oscillations induced by the EWFB are clearly observed.
The spectrum distortion induced by the EWFB at the L1 frequency is amplified around 8 MHz, which is consistent with the applied failure.
EWFB, dynamic scenario. Navys was then configured to generate one fault-free GPS L1 C/A signal at RF. The RF output was plugged into the GEMS RF front end, and acquisition was launched. One minute later, an EWFB distortion with a resonance frequency of 4 MHz and a damping factor of 2 MHz was applied. As for the EWFA experiments, the GEMS measurements were analyzed to verify the correct application of the failure. The code-phase measurements, illustrated in FIGURE 8, show again that the Navys and GEMS clocks are drifting with respect to each other. Moreover, it is clear that the application of the EWFB induced a bias of about 5.2 meters on the code-phase measurement. One should notice that this bias depends upon the chip spacing used for tracking. Matlab simulations were run considering the same chip spacing as for GEMS, and similar tracking biases were observed.
FIGURE 9 shows the ACF produced by GEMS. During the first minute, the ACF looks like a filtered L1 C/A correlation function. Afterward, undulations distort the correlation peak.
Again, additional analysis has been conducted with Matlab, using a GPS signal acquisition and tracking routine. A 40-second accumulation enabled comparison of the faulty and fault-free code chips. FIGURE 10 shows that the faulty code chips are affected by undulations with a period of 244 nanoseconds, which is consistent with the 4 MHz resonance frequency. This temporal signal was then used to compute the spectrum, as shown in FIGURE 11. The figure shows well that the faulty L1 C/A spectrum (red) secondary lobes are raised up around the EWFB resonance frequency, compared to the fault-free L1 C/A spectrum (blue).
FIGURE 10. Fault-free vs EWFB GPS L1 C/A signal.
FIGURE 11. Fault-free vs EWFB GPS L1 C/A signal power spectrum density.
Galileo E1 CBOC(6, 1, 1/11). In the second part of the experiments, Navys was configured to generate the Galileo E1 Open Service (OS) signal instead of the GPS L1 C/A signal. The goal was to assess the impact of EWs on such a modernized signal.
EWFA, static scenario. First, the same Galileo E1 BC signal was generated using two different Navys channels. One was affected by EWFA, and the other was not. The spectra of the obtained signals were observed using a spectrum analyzer. The spectrum of the signal produced by the fault-free channel shows the BOC(1,1) main lobes, around 1 MHz, and the weaker BOC(6,1) main lobes, around 6 MHz. The power spectrum of the signal produced by the EWFA channel has a lag of 5 samples at 120 times f0 (40 nanoseconds). Again, spikes appear at intervals of f0, which is consistent with theory. The signal produced by the same channel, but with a lag set to 21 samples (171.07 nanoseconds) was also seen. Such a lag should not be experienced on CBOC(6,1,1/11) signals as this lag is longer than the BOC(6,1) subcarrier half period (81 nanoseconds). This explains the fact that the BOC(6,1) lobes do not appear anymore in the spectrum.
EWFB, static scenario. The same experiments as for EWFA were conducted for EWFB. Fault-free and faulty (EWFB with a resonance frequency of 8 MHz and a damping factor of 7 MHz) signals were simultaneously generated and observed using the spectrum analyzer. The spectrum distortion induced by the EWFB at the E1 frequency was evident. The spectrum is amplified around 8 MHz, which is consistent with the applied failure.
EWFA, dynamic scenario. The same scenario as for the GPS L1 C/A signal was run with the Galileo E1 signal: first, for a period of one minute, a fault-free signal was generated, followed by a period of one minute with the faulty signal. GEMS was switched on and acquired and tracked the two-minute-long signal. Its code-phase measurements, shown in FIGURE 12, reveal a tracking bias of 6.2 meters. This is consistent with theory, where the set lag is equal to 40 nanoseconds (12.0 meters). GEMS-produced ACFs show the distortion of the correlation function in FIGURE 13. The distortion is hard to observe because the applied lag is small.
FIGURE 12. GEMS code-phase measurements on Galileo E1 pilot signal, EWFA dynamic scenario.FIGURE 13. GEMS ACF computation on Galileo E1 pilot signal, EWFA dynamic scenario.
A modified version of the GPS signal acquisition and tracking Matlab routine was used to acquire and track the Galileo signal. It was configured to accumulate 50 seconds of fault-free signal and 50 seconds of a faulty signal. This operation enables seeing the signal in the time domain, as in FIGURE 14. Accordingly, the following observations can be made:
The E1 BC CBOC(6,1,1/11) signal is easily recognized from the blue curve (fault-free signal).
The EWFA effect is also seen on the BOC(1,1) and BOC(6,1) parts. The observed lag is consistent with the scenario (five samples at 120 times f0 ≈ 0.04 chips).
The lower part of the BOC(6,1) seems absent from the red signal. Indeed, the application of the distortion divided the duration of these lower parts by a factor of two, and so multiplied their Fourier representation by two. Therefore, the corresponding main lobes should be located around 12 MHz. At the receiver level, the digitization is being performed at 25 MHz; this signal is close to the Shannon frequency and is therefore filtered by the anti-aliasing filter.
FIGURE 14. Fault-free vs EWFA Galileo E1 signal.
The power spectrum densities of the obtained signals were then computed. FIGURE 15 shows the CBOC(6,1,1/11) fault-free signal in blue and the faulty CBOC(6,1,1/11) signal, with the expected spikes separated by 1.023 MHz.
FIGURE 15. Fault-free vs. EWFA Galileo E1 signal power spectrum density.
It is noteworthy that the EWFA has been applied to the entire E1 OS signal, which is B (data component) minus C (pilot component). EWFA could also affect exclusively the data or the pilot channel. Although such an experiment was not conducted during our research, Navys is capable of generating EWFA on the data component, the pilot component, or both.
EWFB, dynamic scenario. In this scenario, after one minute of a fault-free signal, an EWFB, with a resonance frequency of 4 MHz and a damping factor of 2 MHz, was activated. The GEMS code-phase measurements presented in FIGURE 16 show that the EWFB induces a tracking bias of 2.8 meters. As for GPS L1 C/A signals, it is to be noticed that the bias induced by EWFB depends upon the receiver characteristics and more particularly the chip spacing used for tracking.
FIGURE 16. GEMS code-phase measurements on Galileo E1 pilot signal, EWFB dynamic scenario.
The GEMS produced ACFs are represented in FIGURE 17. After one minute, the characteristic EWFB undulations appear on the ACF.
FIGURE 17. GEMS ACF computation on Galileo E1 pilot signal, EWFB dynamic scenario.
In this case, signal accumulation was also performed to observe the impact of EWFB on Galileo E1 BC signals. The corresponding representation in the time domain is provided in FIGURE 18, while the Fourier domain representation is provided in FIGURE 19. From both points of view, the application of EWFB is compliant with theoretical models. The undulations observed on the signal are coherent with the resonance frequency (0.25 MHz ≈ 0.25 chips), and the spectrum also shows the undulations (the red spectrum is raised up around 4 MHz).
FIGURE 18. Fault-free vs EWFB Galileo E1 signal.FIGURE 19. Fault-free vs. EWFB Galileo E1 signal power spectrum density.
Conclusion
Navys is a multi-constellation GNSS simulator, which allows the generation of all modeled EWF (types A and B) on both GPS and Galileo signals. Indeed, the Navys design makes the EWF application independent of the signal modulation and carrier frequency.
The International Civil Aviation Organization model has been adapted to Galileo signals, and the correct application of the failure modes has been verified through RF simulations. The theoretical effects of EWF types A and B on waveforms, spectra, autocorrelation functions and code-phase measurements have been confirmed through these simulations.
For a given lag value, the tracking biases induced by type A EWF distortions are equal on GPS and Galileo signals, which is consistent with theory.
Eventually, for a given resonance frequency-damping factor combination, the type B EWF distortions induce a tracking bias of about 5.2 meters on GPS L1 C/A measurements and only 2.8 meters on Galileo E1 C measurements. This is mainly due to the fact that the correlator tracking spacing was reduced for Galileo signal tracking (± 0.15 chips instead of ± 0.5 chips). (Additional figures showing oscilloscope and spectrum analyzer screenshots of experimental results are available in the online version of this article.)
Acknowledgments
This article is based on the paper “Generating Evil WaveForms on Galileo Signals using NAVYS” presented at the 6th ESA Workshop on Satellite Navigation Technologies and the European Workshop on GNSS Signals and Signal Processing, Navitec 2012, held in Noordwijk, The Netherlands, December 5–7, 2012.
Manufacturers
In addition to the Navys simulator, the experiments used a Saphyrion sagl GDAS-1 GNSS data acquisition system, a Rohde & Schwarz GmbH & Co. KG RTO1004 digital oscilloscope, and a Rohde & Schwarz FSW26 signal and spectrum analyzer.
MATHIEU RAIMONDI is currently a GNSS systems engineer at Thales Alenia Space France (TAS-F). He received a Ph.D. in signal processing from the University of Toulouse (France) in 2008.
ERIC SENANT is a senior navigation engineer at TAS-F. He graduated from the Ecole Nationale d’Aviation Civile (ENAC), Toulouse, in 1997.
CHARLES FERNET is the technical manager of GNSS system studies in the transmission, payload and receiver group of the navigation engineering department of the TAS-F navigation business unit. He graduated from ENAC in 2000.
RAPHAEL PONS is currently a GNSS systems engineering consultant at Thales Services in France. He graduated as an electronics engineer in 2012 from ENAC.
HANAA AL BITAR is currently a GNSS systems engineer at TAS-F. She graduated as a telecommunications and networks engineer from the Lebanese Engineering School of Beirut in 2002 and received her Ph.D. in radionavigation in 2007 from ENAC, in the field of GNSS receivers.
FRANCISCO AMARILLO FERNANDEZ received his Master’s degree in telecommunication engineering from the Polytechnic University of Madrid. In 2001, he joined the European Space Agency’s technical directorate, and since then he has worked for the Galileo program and leads numerous research activities in the field of GNSS evolution.
MARC WEYER is currently working as the product manager in ELTA, Toulouse, for the GNSS simulator and recorder.
Additional Images
GEMS graphical interface.Observation of EWF type A on GPS L1 C/A signal with an oscilloscope.Impact of EWF A on GPS L1 C/A signal spectrum for 0 (green), 41 (black), and 171 (blue) nanosecond lag.Observation of EWF type A on GPS L1 C/A signal with an oscilloscope.Impact of EWF B on GPS L1 C/A signal spectrum for fd = 8 MHz and σ = 7 MHz.Impact of EWF A on Galileo E1 BC signal spectrum for 0 (green), 40 (black), and 171 (blue) nanosecond lag.Navys hardware equipment – Blackline edition.
Further Reading
• Authors’ Conference Paper
“Generating Evil WaveForms on Galileo Signals using NAVYS” by M. Raimondi, E. Sénant, C. Fernet, R. Pons, and H. AlBitar in Proceedings of Navitec 2012, the 6th ESA Workshop on Satellite Navigation Technologies and the European Workshop on GNSS Signals and Signal Processing, Noordwijk, The Netherlands, December 5–7, 2012, 8 pp., doi: 10.1109/NAVITEC.2012.6423071.
• Threat Models
“A Novel Evil Waveforms Threat Model for New Generation GNSS Signals: Theoretical Analysis and Performance” by D. Fontanella, M. Paonni, and B. Eissfeller in Proceedings of Navitec 2010, the 5th ESA Workshop on Satellite Navigation Technologies, Noordwijk, The Netherlands, December 8–10, 2010, 8 pp., doi: 10.1109/NAVITEC.2010.5708037.
“Estimation of ICAO Threat Model Parameters For Operational GPS Satellites” by A.M. Mitelman, D.M. Akos, S.P. Pullen, and P.K. Enge in Proceedings of ION GPS 2002, the 15th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 24–27, 2002, pp. 12–19.
• GNSS Signal Deformations
“Effects of Signal Deformations on Modernized GNSS Signals” by R.E. Phelts and D.M. Akos in Journal of Global Positioning Systems, Vol. 5, No. 1–2, 2006, 9 pp.
“Robust Signal Quality Monitoring and Detection of Evil Waveforms” by R.E. Phelts, D.M. Akos, and P. Enge in Proceedings of ION GPS-2000, the 13th International Technical Meeting of the Satellite Division of The Institute of Navigation, Salt Lake City, Utah, September 19–22, 2000, pp. 1180–1190.
“A Co-operative Anomaly Resolution on PRN-19” by C. Edgar, F. Czopek, and B. Barker in Proceedings of ION GPS-99, the 12th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, September 14–17, 1999, pp. 2269–2271.
• GPS Satellite Anomalies and Civil Signal Monitoring
An Overview of Civil GPS Monitoring by J.W. Lavrakas, a presentation to the Southern California Section of The Institute of Navigation at The Aerospace Corporation, El Segundo, California, March 31, 2005.
• Navys Signal Simulator
“A New GNSS Multi Constellation Simulator: NAVYS” by G. Artaud, A. de Latour, J. Dantepal, L. Ries, N. Maury, J.-C. Denis, E. Senant, and T. Bany in Proceedings of ION GPS 2010, the 23rd International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 21–24, 2010, pp. 845–857.
“Design, Architecture and Validation of a New GNSS Multi Constellation Simulator : NAVYS” by G. Artaud, A. de Latour, J. Dantepal, L. Ries, J.-L. Issler, J. Tournay, O. Fudulea, J.-M. Aymes, N. Maury, J.-P. Julien , V. Dominguez, E. Senant, and M. Raimondi in Proceedings of ION GPS 2009, the 22nd International Technical Meeting of the Satellite Division of The Institute of Navigation, Savannah, Georgia, September 22–25, 2009, pp. 2934–2941.
The SGX GPS satellite signal simulator from CAST Navigation provides the user with dynamic, repeatable GPS RF signals for use in the laboratory or in the field for a wide range of GPS applications. The SGX simulator is housed in a portable, lightweight handheld enclosure measuring 7 × 11× 3 inches and weighing just over 4 pounds.
The SGX replaces the CAST-SIMCOM simulator, a 17-inch, 50-pound simulator. The SGX is lightweight, portable, operates on AC or battery power, features 16 channels of L1 C/A- and P-codes, and is extremely accurate and repeatable. It is based on CAST technology that has been developed for use in the company’s larger military products.
The SGX is controlled via an intuitive touchscreen interface that allows the user to select start and stop scenarios, change screen views, and change satellite RF power levels while a scenario is running. Three test scenarios are delivered with the simulator.
XGen Scenario Generation Software. This optional software gives the user the ability to generate custom scenarios for use with the SGX. The software allows for complete control over GPS almanac, ephemeris, and all satellite error sources, including multipath. The user can select from a variety of vehicle types and simulate static or dynamic motion for land, sea, air, and space-based vehicles. The user may also employ antenna gain patterns and vehicle silhouettes.
IFEN Inc., located in California, offers the IFEN NavX-NCS (Navigation Constellation Simulator), a premium-grade RF GNSS simulator. It is available in either the Essential or Professional version, tailored to the customer’s individual test and research needs.
The NavX-NCS Essential simulates dynamic and static GNSS scenarios with up to 42 channels in the upper L-band (such as GPS / SBAS L1, GLONASS G1, Galileo E1, BeiDou B1, and QZSS / IMES L1) and is designed mainly for product testing and system integration.
The NavX-NCS Professional offers up to 108 signal channels in virtually all frequencies and signals. Superior simulation options such as various feared events can be performed by the NavX-NCS Professional.
Both versions of NavX-NCS offer high precision and numerous simulation options. Intuitive, user-friendly software makes test scenarios easy, fast, and clear, whether pre-defined or set up individually.
These IFEN simulators have a unique modular hardware and software architecture, which offers great flexibility when it comes to changes in a company’s strategic test requirements. Every NavX-NCS is fully upgradeable, not only in the number of RF signal channels or available frequencies, but in a possible subsequent supplement of up to four RF outputs.
Designed to be a versatile yet affordable satellite simulator, the GPSG-1000 is used by those validating and testing GNSS receivers in a variety of applications in the transportation, consumer electronics, aerospace, and military industry segments, to name a few. The GPSG-1000 is a single carrier, multi-channel GPS/Galileo simulator. Portable and ruggedized, it can be safely and confidently deployed in a variety of outdoor and indoor environments. The unit is available in a 6- or 12-channel configuration, and supports L1, L1C, L2C, L5, E1, E5, E5a, E5b, and SBAS (WAAS and EGNOS) signals.
The GPSG-1000 can be directly connected to a GNSS receiver under test. It can also simulate actual “open sky” situations, whereby the unit can generate its signals through the included antenna coupler system that isolates and transmits to the UUT’s antenna(s). Utilizing an integrated GPS receiver, the GPSG-1000 simulates actual time of day and date as well as the real constellation that would be available for navigation at that specific time. Multiple almanacs and route files can be saved to memory, enabling current and past history dynamic motion, constellation environment creation/recreation, and other troubleshooting capabilities. During any given static or dynamic simulation, space vehicle parametrics and health can be user controlled.
At less than 10 pounds, the GPSG-1000 features a touch-screen user interface that can be remotely hosted via an integrated Ethernet port. The unit uses a rechargeable, lithium-ion battery, enabling hours of untethered use, or can be used while the battery is recharging.
LabSat and LabSat 2 are record and replay multi-constellation GNSS simulators. Designed as lightweight, easy-to-use standalone systems, the LabSat range has the ability to provide and deliver solutions for a wide range of testing requirements. Equipped with pre-recorded test scenarios and the ability to record and replay the user’s specific scenarios, LabSat delivers precise and accurate test results — replicating real-world situations in the lab or testing facility.
The range also has the ability to record and replay data from a wide range of data sources, including vehicle CANbus, inertial sensors — such as gyrometers and wheel speed sensors — and reference receivers. A recent innovation is the LabSat Turntable Solution, which allows for the replay of dead-reckoning turn-rate signals to be played into a navigation device to simulate use through built-up areas and urban canyons. Both LabSat and LabSat 2 can observe all satellites in view; while LabSat observes GPS, Gallileo, and SBAS, LabSat 2 includes GLONASS and BeiDou-2.
Reliability of the gathered data can be assured when used together with SatGen v2, Racelogic’s simulation software. This scenario-generation software enables users to create scenario files based on user-defined trajectories, which can be replayed on LabSat. With SatGen v2, a scenario can be generated anywhere in the world, with position, route, speed, and time defined by the user. This high-performance software allows users to verify that their GNSS equipment performs as required in a variety of locations that maybe geographically remote or unavailable due to hostile environments.
Rohde & Schwarz R&S SMBV100A: GNSS Simulator on Vector Signal Generator
Rohde & Schwarz
R&S SMBV100A: GNSS Simulator on Vector Signal Generator
The GNSS simulator in the vector signal generator R&S SMBV100A is designed for development, verification, and production of GNSS chipsets, modules and receivers. The simulator supports all possible scenarios, from simple setups with individual, static satellites all the way to flexible scenarios generated in real time with up to 24 dynamic GPS, GLONASS, and Galileo satellites.
GNSS simulator with support of GPS L1/L2 (C/A- and P-code), GLONASS L1/L2, and Galileo E1, including hybrid constellations.
Simulation of realistic constellations with up to 24 satellites in real time (no precalculated waveforms).
Flexible scenario generation including moving scenarios (import of NMEA waypoints), multipath, dynamic power control, and atmospheric modeling without the need for additional software tools.
Unlimited simulation time with automatic, on-the-fly exchange of satellites.
User mode in the GNSS simulator for full flexibility to select the satellites and to define the navigation data (import of RINEX files).
Full localization for static and moving receivers with up to eight satellites with GPS P-code plus commercial C/A-code for complete testing of military receivers.
Support of predefined and user-defined A-GPS test scenarios, including generation of assistance data.
Easy way for time synchronous setup with two instruments for L1 and L2 band simulation.
Spectracom’s line of simulators provides fast, comprehensive navigational, position, and timing testing for devices with GPS receivers. Designed for manufacturers and development engineers, Spectracom’s simulators provide complete testing of multi-channel GPS signal performance with high throughput and ease of use without unnecessary complexity or expense. All GSG-5 and GSG-6 series models are portable and fully operational via front-panel, web-based remote control, or SCPI protocol, and they operate with StudioView for easy scenario creation and file management. Most models are software upgradeable so users can easily add features as requirements change.
GSG-5 Series. The GSG-5 series is a GLONASS and GPS constellation simulator that provides the basic set of features for testing GNSS systems. With a base of four channels, upgradable to 8, 16, or more, it provides navigational fix and position testing for in-line product testing or basic engineering and development testing.
Versatile multi-channel GLONASS + GPS signal generator with pre-configured test scenarios.
Includes advanced features such as SBAS (WAAS, EGNOS, MSAS, or GAGAN), white noise generation, and multipath simulation.
GSG-6 Series (pictured). The GSG-Series 6 family offers multiple frequency band operation, multiple GNSS constellation simulation, and expansion to many more channels. Incorporating all of the features of the popular Series 5 family, the Series 6 line expands the capability to simulate all the new, emerging GNSS signals. With a base of 32 channels, upgradable to 48, 64, or more, it provides navigational fix and position testing for engineering and development testing.
GPS standard, new (L2C, L5) GLONASS, Galileo, and Beidou/Compass coming soon.
Simultaneous multi-frequency P-code (unencrypted) and C/A code.
Simultaneous GNSS Constellation P-code and C/A codes.
Spirent provides simulators that cover all applications, including research and development, integration/verification, and production testing.
GSS8000 (pictured). Spirent’s flagship simulator, the GSS8000, is fully approved for Y-code, SAASM, AES M-code, and SDS M-code testing. Spirent provides options and configurations for testing GNSS interference effects and interference mitigation techniques, such as integrated GPS/inertial testing, CRPA testing, and jamming/anti-jam simulation.
Spirent has delivered simulators that produce both legacy signals as well as modernized signals such as 2C, L5, and L1C. In addition to GPS, systems can include GLONASS L1/L2, Galileo, and Beidou-2, plus SBAS (WAAS, MSAS, and EGNOS) and Japan’s QZSS.
CRPA Test System. Spirent’s Controlled Reception Pattern Antenna (CRPA) Test System generates both GPS L1/L2 and interference signals; multiple GSS8000 chassis may be combined to coherently control up to seven antenna elements. Null-steering and space/time adaptive CRPA testing are both supported by this comprehensive approach.
GSS7790. Spirent’s GSS7790 Multi-Output Simulation System allows the signal from each satellite to be mapped to a separate RF output. These signals can then be fed to individual transmit antennas, which, when suitably deployed in an anechoic chamber, replicate the spatial diversity of satellite and jammer signals incident on the receiver antenna. Additional flexibility is offered as the signal is further split into its GPS L1 and L2 components, as appropriate.
GNSS is changing. The days of only American GPS satellites providing signals to the civilian population are gone as new constellations are launched. GLONASS was a slow starter, but is now well established, and its signal architecture is now commonly implemented in manufacturers’ chipsets. Galileo is still very much in test phase with global coverage planned for 2019, although position fix using only Galileo satellites has already been demonstrated. The Japanese QZSS system, designed to aid navigation in urban canyons, is partially operational with further launches announced for the near future.
The latest openly documented network to come online is BeiDou-2, or BDS. Formerly known as Compass, the Chinese constellation now provides signals to China and surrounding areas, but plans for global coverage should come to fruition by the end of the decade.
Full control over its own constellation gives a country military, socio-political, and commercial advantages, especially if additional functionality — such as search and rescue services — is introduced alongside the standard navigational broadcast. BDS is unique in its use of a combination of standard-orbit and geo-synchronous satellites, the latter giving it a wider range of signal designed to carry more information.
The populace stands to benefit from a wide variety of localized and global satellite coverage, but only if there are end-user products available that actually make use of the new signals. Any manufacturer wanting a share of the market in China, for instance, will need to get BeiDou-2 integrated into its chipsets quickly, especially if an import levy is placed upon devices that don’t support it (as nearly happened with GLONASS).
How do you go about implementing BDS support in your new GPS product if you’re based in Europe or America? The coverage isn’t global yet; you can’t just go out into the office car park to test, and how are you going to incorporate the signals from the three geostationary satellites without actually being underneath them? Moving to China isn’t very practical, so the solution is a GNSS record-and-replay device.
Manufacturers and other customers will want to seek out simulators from companies that have been highly proactive in ensuring their products provide full support for each constellation, even before they come fully online. The convenience in being able to test new designs, applications, and system integration with reliability and consistency can bring significant savings in development cost and time.
With 14 BDS satellites currently in operation, and the recent release of the Interface Specification, we find more and more companies in the marketplace have been asking for BeiDou functionality. An added benefit for existing users would be flexible hardware capable of taking a simple firmware upgrade in order to record and replay BeiDou as well as GPS and GLONASS.
Icing on the system-testing cake would be a hard drive containing pre-recorded scenarios from China and Europe, with good BDS visibility, so that bench testing can commence immediately. Given that such a device can record raw signals, live recordings can be taken in Asia and then transferred to test facilities around the world.
Mark Sampson is Racelogic’s LabSat product manager. He has more than 15 years of experience in the development of GNSS technology. Working closely with leading businesses such as Bosch, Intel, Samsung, and Telefonica, he provides knowledge and expertise in testing any GNSS device, application, or integration.
GNSS have been with us for more than 30 years, giving rise to a wealth of positioning and navigation technologies for military, civilian, and consumer use. Today, we’re entering a new era of experimentation and innovation in satellite and hybrid positioning. In turn, this drives new test challenges and introduces an ever wider group of engineers to the art and science of GNSS test.
Where Is the Testing Panacea? I am sometimes asked, “What is the best way of testing a GPS receiver?” — as if there existed some laboratory panacea to all GNSS test and characterization woes. Well, there is a saying, “There are horses for courses,” meaning what performs well in one situation may not deliver in another, and nowhere is this more true than in the field of GNSS test. Not only is there a wide range of different test equipment available, but there are no universally applicable test objectives, test methods, or parameter definitions, in exactly the same way as there is not one universally applicable GNSS receiver. Just as the rapid time-to-first-fix of an automotive receiver may be less relevant in a maritime environment, so different test approaches have their place.
A Systematic Approach. If there is one thing, it is this: be systematic in your test design. Consider the purpose of the test, the test conditions, and the measurements you plan to take, and be wary of generic tests that may not achieve your objectives.
Equipment. A wide range of GNSS testing equipment is available, ranging from basic single-constellation RF simulators to highly configurable, multi-GNSS constellation simulators. Single-channel, multi-channel, and record and playback systems all have their place, and to get the best results in the fastest time, it’s essential to choose the kit that’s right for the kind of testing you need to do.
Vulnerability, Fidelity, Integrity, and Time Travel. More and more, receivers need to be tested for their vulnerability to interference, jamming, and spoofing. As GNSS-derived position and time become more ubiquitous, so the motivation for confounding the system grows. This has a double impact on test.
First, performance requirements around vulnerability may be introduced, with tests to match. Second, and perhaps less obvious, is the way in which this concern is reflected in the receiver’s design and potential rejection of the laboratory test signal. Yes, I mean receivers getting more fussy about the signals they lock onto. Anyone who has tested a receiver with an out-of-date recording or simulation scenario will have experienced a receiver refusing to track a satellite showing a time and date prior to its firmware release date. The receiver, discounting time travel, knows there has to be something wrong with a satellite showing a date before it was born. With the risk of spoofing, receivers will only get more picky and likely to reject poorly simulated signals. To avoid such effects, it is important to have very high integrity and fidelity in any simulator system. Getting these details right is not esoteric, but is essential to allow the proper attribution of any problems observed and if test results are to have any meaning.
Conclusion. Be systematic in your approach to test; beware the universal and generic; “good enough,” it rarely is.
Steve Hickling is lead product manager for Spirent’s GNSS simulator business and is based at the factory in Paignton, England. Previously he held a variety of marketing, technical, and management roles in the telecoms and optical components industries. He holds a bachelor of science degree in physics and electronic engineering from Birmingham University, an MBA from Open University Business School, and a post-graduate diploma from the Chartered Institute of Marketing.
By Pierre Nemry and Jean-Marie Sleewaegen, Septentrio Satellite Navigation
Today’s customers ask for high-accuracy positioning everywhere, even in the most demanding environments. The time is long gone that the only requirement for a receiver was to track GPS L1 and L2 signals in open-sky conditions. State-of-the-art receivers operate in increasingly difficult conditions, cope with local radio-frequency interference, survive non-nominal signal transmissions, decode differential corrections from potentially untrusted networks — and more!
Difficult real-life operating conditions are typically not addressed in textbooks or in the specialized literature, and yet they constitute the real challenge faced by receiver manufacturers. Most modern GNSS receivers will perform equally well in nominal conditions, or when subjected to nominally degraded conditions such as the ones that correspond to standard multipath models. However, the true quality of a GNSS receiver reveals itself in the environment in which it is intended to be used.
In view of this, a GNSS manufacturer’s testing revolves around three main pillars:
◾ identifying the conditions and difficulties encountered in the environment of the intended use,
◾ defining the relevant test cases, and
◾ maintaining the test-case database for regression testing.
In developing new receiver functionality, it is important to involve key stakeholders to comprehend the applications in which the feature will be used and the distinctive environment in which the receiver will function. For example, before releasing the precise-point-positioning (PPP) engine for the AsteRx2eL, we conducted a field-test campaign lasting a full month on a ship used for dredging work on the River Thames and in the English Channel. This enabled engineers to capture different types of sea-wave frequency and amplitude, assess multipath and signal artifacts, and characterize PPP correction data-link quality.
Most importantly, we immersed the team in the end-user environment, on a work boat and not simply in a test setup for that purpose. As another example, in testing our integrated INS/GNSS AsteRxi receiver for locating straddle carriers in a container terminal, we spent months collecting data with the terminal operator. This was necessary to understand the specificities of a port environment, where large metal structures (shore cranes, container reach-stackers, docked ships) significantly impair signal reception.
Furthermore, the close collaboration between the GNSS specialist, the system integrator, and the terminal owner was essential to confirm everything worked properly as a system. In both examples, in situ testing provide invaluable insight into the operating conditions the receivers have to deal with, much surpassing the possibilities of a standard test on a simulator or during an occasional field trip.
Once an anomaly or an unusual condition has been identified in the field, the next step is to reproduce it in the lab. This involves a thorough understanding of the root cause of the issue and leveraging the lab environment to reproduce it in the most efficient way. Abnormalities may be purely data-centric or algorithmic, and the best approach to investigate and test them would be software-based. For example, issues with non-compliance to the satellite interface control document or irregularities in the differential correction stream are typically addressed at software level, the input being a log file containing GNSS observables, navigation bits, and differential corrections.
Other issues are preferably reproduced by simulators, for example those linked to receiver motion, or those associated to a specific constellation status or location-dependent problems. Finally, certain complicated conditions do not lend themselves to being treated by simulation. For example, the diffraction pattern that appears at the entrance of a tunnel is hard to represent using standard simulator scenarios. For these circumstances, being able to record and play back the complete RF environment is fundamental.
Over the years, GNSS receiver manufacturers inventoried numerous cases they encountered in the field with customers or during their own testing. For each case, once it has been modeled and can be reproduced in the lab, it is essential to keep it current. As software evolves and the development team changes, the danger exists that over time, the modifications addressing a dysfunctional situation get lost, and the same problem is reintroduced. This is especially the case for conditions that do not occur frequently, or do not happen in a systematic way. Good examples are the GLONASS frequency changes, which arise in an unpredictable way, making it very difficult for the receiver designer to properly anticipate. This stresses the importance of regression testing. It is not enough to model all intricate circumstances for simulation, or to store field-recorded RF samples to replay later. It is essential that the conditions of all previously encountered incidents be recreated and regularly tested in an automated way, to maintain and guarantee product integrity.
The coverage of an automated regression test system must range from the simplest sanity check of the reply-to-user commands to the complete characterization of the positioning performance, tracking noise, acquisition sensitivity, or interference rejection. Every night in our test system, positioning algorithms including all recent changes are fed with thousands of hours of GNSS data, and their output compared to expected results to flag any degradation. Next to the algorithmic tests, hardware-in-the-loop tests are executed on a continuous basis using live signals, constellation simulators, and RF replay systems, with the signals being split and injected in parallel into all our receiver models. Such a fully automated test system ensures that any regression is found in a timely manner, while the developer is concentrated on new designs, and that a recurring problem can be spotted immediately. The test-case database is a valuable asset and an essential piece of a GNSS company’s intellectual property. It evolves continuously as new challenges get detected or come to the attention of a caring customer-support team. Developing and maintaining this database and all the associated automated tests is a cornerstone of GNSS testing.
Spectracom announced its new L1+L2 dual-frequency 32-channel multi-GNSS simulator, the GSG-62. The GSG-62 offers multiple frequency band operation, multiple GNSS constellation simulation, and expansion capability for more frequency bands and channels, the company said.
The new simulator provides expanded capabilities for those who are testing more than GPS L1, according to the company. “We understand the challenges our customers have in fast-paced development, migration and delivery of products with ever changing embedded GNSS receivers,” said John Fischer, Spectracom CTO. “As such, we are excited to introduce this next-generation multi-signal instrument that allows for real-time scenarios, is intuitive to understand, quick to deploy and, given its design to support upgrades to L2C, L5, and future GNSS frequencies and systems, protects our customer’s investment in test gear.”
Fischer continued, “In addition to a wide variety of technical challenges, we also understand our customers must balance the ability to quickly develop solutions and improve cost performance in their operations. We believe the price, unique features, and form factor of the GSG-62 will allow them to do both.”
The GSG-62 is designed for manufacturing and development testing with its ability to simulate all the visible satellites for the receiver under test. With 16 channels for L1 frequency and 16 channels for L2 frequency, channels can be assigned to GPS or GLONASS, P-code or C/A code. Channels may also be used for SBAS simulation of EGNOS, WAAS, GAGAN, or MSAS satellites, or for multipath and interference signals.
The GSG-62 incorporates all the features of Spectracom’s previous models, including compatibility with GSG StudioView PC software for creation and editing of simulation scenarios via Google Maps.
Spectracom is a business of the Orolia Group and provider of practical test solutions for GPS and GNSS devices and systems.
