Airbus will install Orolia’s Ultima-DT emergency locator transmitter on its aircraft. (Photo: Airbus)
This fall, Orolia’s Ultima-DT was certified as an emergency locator transmitter with distress tracking (ELT-DT) by Cospas-Sarsat, an international humanitarian search-and-rescue system. Cospas-Sarsat uses space-based technology to detect and locate model 406 emergency beacons carried by ships, aircraft or individuals venturing into remote areas — often inaccessible by GNSS signals. The system consists of a network of satellites, ground stations, mission control centers and rescue coordination centers that work together when a 406 beacon is activated.
I spoke about the certification with Christian Belleux, director, Aviation & Defense Beacons for Orolia.
Matteo Luccio (ML): Has Orolia produced aviation safety products in the past?
Christian Belleux (CB): Orolia has been supplying emergency locator transmitters for aviation since 1995 on a very large number of platforms to OEMs and airlines for use on commercial aircraft — Airbus, Boeing, Embraer and Bombardier aircraft. Orolia is also participating in industry groups creating standards (Eurocae, RTCA, ARINC) or contributing to the progress of the Cospas-Sarsat search-and-rescue satellite system as a member of the Expert Working Group.
ML: What are the key challenges in making an aviation ELT?
CB: With new requirements for lithium batteries and new regulations introducing distress tracking, recent times have been rich in innovation. We were granted the first ETSO certification ever for an ELT-DT and the same product, the Ultima-DT, was also the first ELT to be certified for its lithium battery.
ML: What did Cospas-Sarsat certification of the ELT-DT entail?
CB: The ELT-DT is a new type of beacon with a new communication protocol. The labs performing the certification tests must be approved by Cospas-Sarsat before we can apply. Then the Cospas-Sarsat organization and infrastructure must be updated to receive and consider the new ELT-DT protocol. The Cospas-Sarsat certification of our ELT-DT means that it complies with the performance requirements described in Cospas-Sarsat standards and can communicate with the infrastructure.
ML: What is new about an ELT-DT?
CB: The principle of an ELT-DT is to activate in flight before a crash, as opposed to a legacy ELT that is activated by the shock of a crash. This means that the aircraft and the ELT-DT can analyze the health of the aircraft and its parameters, and activate if a catastrophic event is about to occur. Once activated, the ELT-DT transmits a high-rate distress signal that makes it possible to track the aircraft until it crashes. The ELT-DT contains its own GNSS receiver that is independent the aircraft’s navigation system.
ML: Did you cooperate closely with one or more avionics manufacturers to develop your device?
CB: Orolia was in very close contact with Airbus, which designed the avionics components.
ML: Do you already have contracts with airlines or aircraft manufacturers besides Airbus for the Ultima-DT?
CB: We have several contacts with aircraft manufacturers and airlines interested in the Ultima-DT.
ML: When will the first batch of the ELT-DT / Ultima-DT be operational?
CB: We started flight tests months ago at Airbus and delivered production units. Airbus soon will announce its first delivery of an aircraft equipped with the Ultima-DT.
Septentrio’s mosaic-T is built specifically for resilient and precise time and frequency synchronization under challenging conditions. (Photo: Septentrio)
Fugro has signed a tri-party cooperation agreement with GNSS receiver company Septentrio and synchronization equipment manufacturer Meinberg to launch the Fugro AtomiChron real-time synchronization and authentication service.
Numerous sectors rely on resilient and highly accurate time synchronization, including telecommunications, finance and energy. The timing technology eliminates time drift caused by clocks counting time at slightly different rates, and provides extreme stability that surpasses current precision frequency standards.
With up to sub-nanosecond accuracy, Fugro AtomiChron includes Navigation Message Authentication (NMA), ensuring reception of genuine GNSS signals and time synchronization improvements. Integrated anti-spoofing detection further prevents interference with GNSS timing signals providing accuracy, authentication, validity and security for end users.
The agreement ensures that the Fugro AtomiChron service will be available in new Septentrio mosaic-T GNSS receivers, as well as a selection of Meinberg GNSS clocks, without the need for additional physical interfaces or separate antennas.
“Septentrio is a forerunner in the area of robust and resilient GNSS solutions,” said Jan Van Hees, business development director at Septentrio. “With the addition of the unique Fugro AtomiChron service, we are pleased to further strengthen our offering and provide our customers even more accurate and reliable solutions for resilient GNSS timing.”
Geometer International, a Ukrainian developer of GNSS/RTK instruments and applications for satellite positioning, has introduced the Walker RTK, a dual-frequency L1, L2 RTK receiver in the compact form factor of a portable RTK device.
The Walker RTK is a lightweight, small-sized, affordable and full-featured device for collecting, storing and processing geo-referenced data on the survey site. According to the developer, a GNSS receiver in a convenient and affordable format will significantly expand the use of RTK technology. The new technology will be suited to most tasks requiring centimeter precision positioning and measurements in a 3D coordinate system.
Compact and lightweight, Walker RTK is the ideal solution for field workers working away from the office. The new device can be operated with just one hand, significantly improving the productivity of service personnel.
Possible applications for GNSS Walker RTK include surveying, utilities, solar power plant engineering, trenching and pipeline installation, drilling, forestry and municipal infrastructure control.
What’s under the bonnet of Walker RTK?
The Walker RTK is built around a 2-frequency L1/L2 184 channel board and a sensitive Helix antenna, satisfying up to 90% of basic user requirements. The tube-shaped housing geometry allows it to fit with any universal mount. The receiver weight is only 0.25g (0.470 with smartphone holder) due to the aluminum alloy housing with a protective coating. The Walker RTK has a built-in Li-Ion battery with enough power for 24 hours of continuous operation without additional recharging. The new energy-efficient architecture of the unit achieves this.
The GNSS receiver has the minimum amount of leading interfaces, resulting in high IP67 dust and waterproof rating. The device can be paired with a smartphone or tablet via Bluetooth, while connection via Bluetooth low energy is also planned for a future release.
Compatible with satellite systems
Walker RTK can track and determine geo-position using signals from all known existing satellite systems. This feature makes it possible to achieve the centimeter-level accuracy of an RTK solution within seconds.
GNSS signals processed by the Walker RTK GNSS receiver:
Thanks to NMEA messaging, the Walker RTK GNSS receiver is fully compatible with any professional or freeware geolocation software, providing high accuracy and reliable RTK-corrected positioning.
The Google Smartphone Decimeter Challenge (SDC) competition, co-sponsored by the Institute of Navigation (ION), took place this summer. For the competition, teams developed high-precision GNSS positioning using a pool of smartphone GNSS + inertial measurement unit (IMU) datasets accompanied by high-accuracy ground truth. Teams competed to achieve the best location accuracy with the datasets provided. Winners received cash prizes and sponsored attendance at the ION GNSS+ 2022 conference in Denver, Sept. 19-23, to present their results.
Origins
The SDC has its origins in the Android Operating System, which is an open-source platform. In 2016, Google made GNSS raw measurements available as a public application programming interface (API) on all Android phones. Since then, the available measurements have become more sophisticated and more accurate. For example, dual-frequency carrier-phase data is now available on many Android phones. This enables new areas of research.
Goals
The competition had two goals:
• Stimulate the research and development of high-accuracy algorithms that can produce submeter position accuracy on phones.
• Establish a publicly accessible repository of labeled data so that all future research on location algorithms can be judged in a consistent way against a standard set of data.
The first goal was met beyond our expectations. A total of 1,381 teams participated in the two competitions of 2021 and 2022. Discussion among competitors on the competition platform (kaggle.com) was wide-ranging, incredibly collegial, and beneficial to the entire community.
Competitors have written and shared detailed descriptions, and these have been reviewed and commented on by other competitors. Moreover, winners have written formally peer-reviewed papers and made presentations at the ION GNSS+ conferences, which are available from ion.org.
The second goal is a work-in-progress and is intended to be the legacy of the events.
Legacy
Disciplines such as machine learning have established benchmarks that make it possible to compare new approaches to previous ones in a proper quantitative way. In the GNSS community, this convention has been missing — a glance across papers at conferences will show that different algorithms tend to be presented with different test data and different metrics. Usually, the authors collect this data, and it is often fairly sparse (one or two drive tests, for example). Also, the reader never knows whether the data was cherry-picked (were bad results not mentioned?).
The SDC data provides:
• 206 different drive tests
•86 total hours of dual-frequency (L1, L5) data with code and carrier-phase measurements
•All labeled with ground-truth positions and velocities collected using NovAtel SPAN ISA-100C, with precise lever-arm compensation and validated with Google’s analysis tools.
The Kaggle site allowed users to submit their results, then automatically scored them against the ground-truth data. We advocate that all GNSS researchers use this resource to measure their location algorithm improvements in a standard way. This creates trust in published results, accelerating the recognition and adoption of truly great improvements for the benefit of the entire industry and GNSS users worldwide.
The top three winners of this year’s Smartphone Decimeter Challenge described their projects to Matteo Luccio, GPS World editor-in-chief.
Suzuki
Taro Suzuki, Chiba Institute of Technology
1st Place Winner: Two-Step Optimization of Velocity and Position using Smartphone’s Carrier Phase Observations
What is your research focus and how does it relate to the contest?
My current research focuses on the accurate positioning of vehicles and mobile robots in urban environments where GNSS multipath occurs. I usually use commercial GNSS receivers for my research. This competition is very relevant to my current research, except that the smartphone is replacing a receiver.
How long have you been developing the technology or approach you used to win the contest?
The competition was held for three months, but I concentrated my efforts on the past three weeks. However, I used technologies and resources developed in my previous research (for example, source code developed in last year’s competition).
Have you participated in previous editions of this contest?
Yes, I participated in the last competition and won. The approach used in this year’s competition is based on the method used to win last year’s competition, with additional innovations and improvements.
Where, in what GNSS signal conditions, and at what speeds were the test data collected?
The competition provides a training dataset, which contains raw GNSS observations from a smartphone installed on a vehicle as it travels on real roads. In addition to GNSS observations, the training dataset contains the ground truth of the smartphone’s position. The training dataset includes a wide range of GNSS signal conditions, such as driving on highways around San Francisco and Los Angeles, driving on tree-lined urban streets, and driving in tunnels and under overpasses. I have developed an algorithm that uses a training dataset containing ground truth to accurately estimate the location of smartphones in a variety of GNSS signal reception environments.
What accuracies were you able to obtain?
The competition metric was “average of 50th and 95th percentile horizontal errors.” The metrics are computed for each of the 36 runs in the test dataset, which are divided into public and private groups, then the metrics are averaged in each group to compute the final score. My final score was 1.382 m for public and 1.229 m for private. The best score given after the competition was 1.372 m for public and 1.197 m for private. The final result achieved sub-meter accuracy in the median (50th percentile).
What are the key features of your approach?
The key point of my method is global optimization using graph optimization, unlike a conventional Kalman filter or least-squares-based positioning methods. In addition, highly accurate relative position estimation using the time difference of carrier wave phases of smartphones contributed to the accuracy. Because the competition dataset included environments such as tunnels and elevated structures in which GNSS cannot be received at all, I devised an algorithm with two optimization steps (first velocity optimization, then position optimization) and applied it to the competition. This method enables highly accurate position estimation for vehicle driving data in various GNSS signal reception environments using only smartphone GNSS observation data.
What end-user applications are you expecting your approach to enable?
Decimeter-accurate location estimation could lead to lane-level navigation for vehicles, pedestrian navigation, and advanced location-based smartphone games.
Dai
Shubin Dai, Kaggle Community
2nd Place Winner: Improving Smartphone GNSS positioning using Gradient Descent Method
What is your research focus and how does it relate to the contest?
I am a data scientist and one of the top competition grandmasters on Kaggle. My research interests include computer vision, natural language processing, autonomous driving, and reinforcement learning. I placed in the top three in 14 related competitions (13 of which were solo). So, despite my lack of background knowledge in the GNSS field, these methods, skills and experiences helped me find a solution.
How long have you been developing the technology or approach you used to win the contest?
I spent about 50 days on this competition, including learning principles of GNSS and understanding all kinds of algorithms by reading books, papers and source codes. The Kaggle platform is very helpful when we want to get started in a new field.
Have you participated in previous editions of this contest?
I did not participate in the competition held last year, but I learned a lot from solutions of recent years, particularly the third-place solution.
Where, in what GNSS signal conditions, and at what speeds were the test data collected?
The benchmark datasets include raw GNSS measurement and raw readings from inertial sensors, using smartphones (Xiaomi Mi 8, Google Pixel 4, etc.) enabled with dual-frequency and ADR (accumulated delta range) in driving scenarios, collected in the San Francisco Bay area.
In the GSDC2021 dataset, there are 29 drives with 73 phone GNSS logs in the training set and 19 drives with 48 phone logs in the test set. Compared to 2021’s competition, in the GSDC2022 dataset we can see more data overall and a wider variety of routes: 62 drives with 170 phone logs are provided in the training set and 36 drives with only one phone per drive are provided in the test set.
The drives in the training set took 15 to 60 minutes at an average speed of 18 m/s.
What accuracies were you able to obtain?
According to the metric of this competition, the score is calculated as the mean of the 50th and 95th percentile distance errors. The score on my local validation set is 1.929 m, the score on the public test set is 1.608 m, and the score on private test set is 1.499 m. When we calculate the mean error, the score is 1.401 m on a validated set, the mean error of 40% of the trips are under 1 m. I think the competition metric is more reliable as the 95th percentile distance error is also important.
By the way, my local validation set is more difficult to optimize than the test set, so the mean error on the test set is expected to be lower than 1.401 m.
What are the key features of your approach?
The competition data is noisy due to multipath effects, non-line-of-sight receptions, receiver noise and missing data, therefore it’s quite challenging. I found that the optimal estimation for each point locally is not stable and can be affected by noise at that point on the track. If we can find a solution to a whole track globally, the noise can be reduced as the model must follow all kinds of constraints, such as geometry constraints, speed constrains, and global acceleration constraints.
Although we could extend the WLS and Kalman filter solution to take more points on a track into consideration, it’s not so easy to model all kinds of constrains. On the other hand, if we use a global optimization method, such as factor graph optimization and neural networks, we can add the constrains easily, which makes it more efficient to conduct experiments.
Following the solution of the third-place winner in last year’s competition, I used the global optimization method by taking into account gradient descent, pseudorange, pseudorange rate, accumulated carrier phase (ADR), phone speed and acceleration constraints of every time epoch on a track. When optimizing the track using gradient descent, the losses are designed to filter out abnormal data and reduce the noise by a series of physical and geometrical rules. I spent much time searching for the constraints, proving them and turning them into losses that can be used to update the coordinates iteratively during the competition.
What end-user applications are you expecting your approach to enable?
According to the setting of this competition, we can post-process data collected using Android phones, which is easily obtained. The track obtained can then be optimized using the solutions from this competition. The solutions from the first and the second place can both be considered as a framework that can be extended by adding more constrains to it to improve accuracy.
Everett
Tim Everett, RTK Consultants LLC
3rd Place Winner: An RTKLIB Open-Source-Based Solution
What is your research focus and how does it relate to the contest?
I develop and maintain the demo5 fork of the popular RTKLIB open-source GPS/GNSS software tool. I have optimized this software for low-cost precision GNSS solutions, so it is very closely related to the goals of this competition. My background is in control system theory and I worked in product and technology development for servo systems in the disk drive industry for 25 years before switching to the GNSS field. The mathematics turns out to be quite similar between the two as both are problems in precision positioning, just different in scale. In disk drives, it is nanometers over centimeters and in precision GNSS, it is centimeters over kilometers.
How long have you been developing the technology or approach you used to win the contest?
I have been developing and maintaining low-cost precision GNSS solutions in the RTKLIB software for about six years but have only worked with smartphone solutions in the last year or two.
Have you participated in previous editions of this contest?
I did not participate in last year’s competition but I did work with the data after the contest was over and shared a solution using RTKLIB that would have placed fifth in the competition.
What accuracies were you able to obtain?
I achieved a score of 1.648 m on the private leaderboard. This represents the average of the 50th percentile and the 95th percentile of the errors as scored by Kaggle. Kaggle does not provide any further breakdown of this number but, based on the training data for which ground truths were provided, this corresponded to a 50th percentile error of roughly 0.9 m and a 95th percentile error of roughly 2.3 m. With a small tweak to my solution after the competition was over, I was able to improve my private leaderboard score to 1.593 m, which would have been within 1 cm of the third-place solution.
What are the key features of your approach?
My approach was to use the existing post-processing kinematic (PPK) solution algorithm in RTKLIB but to reoptimize it for the unique characteristics of the smartphone observation data. A PPK solution is the post-processing equivalent of a real-time kinematic (RTK) solution and is a differential solution that relies on differencing the receiver observations with observations from a nearby base station to cancel out most of the largest error sources — including atmospheric, orbital and clock errors — since these errors are common between the two sets of proximate observations.
Because smartphones have very poor GNSS antennas and they were mounted inside vehicles, the signal quality is much lower and the multipath much greater than those for which the RTKLIB algorithm was optimized. In addition, the smartphones were using the L5 frequency band, whereas RTKLIB was optimized for the more commonly used L2 frequency band. One of the main goals of my optimization process was to include many low-quality observations in the solution that would normally be discarded, but to de-weight them appropriately.
What end-user applications are you expecting your approach to enable?
RTKLIB software is currently used to provide precision solutions for many end-user applications such as surveying, drone photogrammetry, sports tracking, precision agriculture, utility location, marine navigation and ground subsistence monitoring. Although smartphones won’t replace dedicated low-cost GNSS receivers, the challenging nature of the smartphone data severely stresses the RTKLIB algorithms and exposes numerous opportunities for improvement that are much less obvious with more typical, higher quality data. I have pulled these improvements into the main branch of the demo5 version of RTKLIB, and hence this work should immediately improve the quality of all these applications and extend their use into more challenging environments.
Photo: Google
Acknowledgements: Thanks to the Institute of Navigation (ION) for co-sponsoring the 2022 Smartphone Decimeter Challenge. Thanks to Luke Walcher and Tolu Ojelade for their contributions to the photos used in this article.
Interference-free GNSS signals are essential for more than just military vehicles and aircraft. Anti-jam systems usually suppress signals from interference sources by means of spatial filtering.
These solutions can likewise be used to protect satellite navigation signals for autonomous driving and flying against interference signals. To allow GNSS receivers to detect interference sources and suppress transmitted interference signals, they must be designed as multichannel systems.
This way the direction of the interference signal can be determined using phase-coherent signal processing of signals from multiple antennas, and the interference can be suppressed. Rohde & Schwarz offers a solution for the verification of interference immunity and interference suppression.
FIGURE 1a. The GNSS antenna in the example on the left has only one element, so its characteristic cannot be modified. A sufficiently strong interference signal can prevent the receiver from processing the GNSS signals, making satellite-based navigation impossible.FIGURE 1b. In contrast to the individual antenna, the characteristic of the antenna array can be modified by combining and weighting the received signals. The interference signal is suppressed at its angle of arrival, and the GNSS signals can be received. A disadvantage is that GNSS signals from the same direction as the interference signal are also suppressed.
Multi-channel receivers can simultaneously process signals from multiple distributed antennas or from an antenna array. This is useful for determining the direction of incoming signals by means of signal analysis, and for adjusting the antenna pattern so that undesired signals are suppressed. For GNSS-based position determination, this means that signals from global navigation satellite systems (GNSS) can be strengthened and jamming or spoofing signals originating from the ground or the air can be suppressed. Up to now this technology has primarily been used for military applications, but in the future it can also make an important contribution to robust navigation for autonomous driving or flying. Typical interference sources in this regard are harmonics of transmitters in the vicinity, tactical air navigation (TACAN) signals, DME air navigation signals for civil aviation, and LTE signals. Another factor is the growing popularity of so-called personal privacy devices (PPD), which are GNSS jammers that radiate narrowband or broadband signals to disrupt GNSS localization. A new solution from Rohde & Schwarz enables comprehensive testing of the resistance of GNSS receivers to interference signals, if necessary in a realistic hardware-in-the-loop (HIL) environment.
Multi-Channel GNSS Receivers for Interference Suppression
GNSS receivers often use controlled reception pattern antennas (CRPA) to suppress undesired signals. These antennas consist of an antenna array and a signal processing unit. The connected antennas are generally arranged in a strict geometric pattern to achieve full coverage of all possible signal directions. The overall receive characteristic of the antenna array can be altered by suitable weighting of the signals from the individual antennas in the signal processing unit (Fig. 1). This way, interference signals can be specifically blanked out (nulling) or the required GNSS signals can be amplified at their angle of arrival (beamforming). A combination of these two methods is also possible. The antenna arrays typically consist of four to seven elements. The number of interference signals that can be simultaneously suppressed increases with the number of elements.
FIGURE 2a. A four-channel GNSS test system consisting of two R&S SMW200A vector signal generators and an R&S SMA100B analog signal generator for the LO signal (left). The vector network analyzer is used to calibrate the overall system at a user-selectable reference plane in terms of amplitude, phase and propagation time.FIGURE 2b. A four-channel GNSS test system consisting of two R&S SMW200A vector signal generators and an R&S SMA100B analog signal generator for the LO signal (left). The vector network analyzer is used to calibrate the overall system at a user-selectable reference plane in terms of amplitude, phase and propagation time.
Test System Requirements
Rohde & Schwarz offers a test system for GNSS receivers that use CRPAs. First, it acts as a multichannel GNSS simulator that considers all aspects of a satellite navigation system. It must be able to generate the signals of all standard satellite navigation systems in all GNSS frequency bands, with attention to correct satellite orbits, signal propagation characteristics and realistic modeling of the dynamically changing receive environment. Configuration of the antenna array in terms of geometry and the receive characteristics of the individual antennas also must be included.
Simulating the Interference Signals
Second, the system can simultaneously generate jamming or spoofing signals in order to test the interference suppression functions of the device under test (DUT). A second, identical test system is necessary for freely definable configuration of interference sources with very high transmit power. Here the R&S Pulse Sequencer software assists in the definition of complex interference scenarios. The scenarios cover requirements such as long simulation times, moving interference sources and GNSS receivers, user-defined antenna patterns and antenna scans. In addition, the software calculates the correct amplitude, phase angle and propagation time of the signals as a function of signal frequency, antenna arrangement, and the positions of transmitters and receivers in three-dimensional space for each individual antenna element. Signal generation is handled by the R&S SMW200A high-end vector signal generator.
For the tests, the required GNSS signal as well as the unwanted interference signals must be generated for each antenna input of the GNSS receiver. In order to test a CRPA receiver with four antenna inputs, this means that four signal sources are needed to generate the GNSS signals and an additional four signal sources are needed to generate the interference signals. Fig. 2 shows a pair of test systems that can be used to generate coupled GNSS signals and interference signals for a four-channel CRPA receiver.
Calibration Against the DUT
In order to correctly simulate the directions of the satellite signals and the interference signals, the test systems must be calibrated at the RF interface to the DUT with regard to amplitude, phase and propagation time. This means that the amplitude, phase and propagation time differences between the individual RF paths, resulting for example from cables or RF components, must be compensated. The vector signal generators of each system are phase coherently linked using suitable synchronization. A high-end R&S SMA100B analog signal generator in each system provides the shared LO signal.
Using the R&S RF Ports alignment software, the complete system can be calibrated at any desired reference plane with regard to amplitude, phase and propagation time, so that the properties of the test system do not corrupt the simulated signal differences between the individual antennas. The required measurements are performed with a vector network analyzer.
It is not necessary to calibrate the two test systems relative to each other. For the simulation of realistic scenarios, it is sufficient to run the GNSS and interference source simulations at the same time, since in the real world there is usually no correlation between GNSS satellites and interference sources.
FIGURE 3. Aircraft with a multichannel radar warning system consisting of multiple receive channels, a central processing unit and a display.
Integration in an HIL Environment
The GNSS test system also can be embedded in a hardware-in-the-loop (HIL) environment. In this case a computer streams the motion profile of the GNSS receiver under test, with position, speed, acceleration and vehicle attitude, to the test system at a high data rate. The test system then generates the corresponding satellite navigation signal in real time. This requires very high update rates and low latencies.
Summary
Multichannel GNSS CRPA receivers considerably improve the navigation of ground vehicles and aircraft of all kinds. With the new Rohde & Schwarz test system, realistic multi-channel test signals can be generated for both GNSS simulation and interference simulation. For tests in an HIL environment, motion data also can be streamed to the GNSS test system.
Hemisphere GNSS has announced another Vega heading and positioning OEM board using the Lyra II and Aquila chipsets.
The Vega 60 GNSS board fits industry-standard 46 x 71 mm form factors with a 60-pin connector. It can be used to replace more expensive and lesser abled 60-pin boards with either single- or dual-antenna capabilities.
Hemisphere’s Lyra II and Aquila application-specific integrated circuit (ASIC) designs provide the ability to simultaneously track and process more than 1,100 channels from all GNSS constellations and signals including GPS, GLONASS, Galileo, BeiDou, QZSS, NavIC, SBAS and L-band. The ASIC technology offers Vega 60 scalable access to every modern GNSS signal available.
Cygnus interference mitigation technology is also a standard feature, providing built-in digital filtering capabilities and spectrum analysis. This provides enhanced anti-jamming as well as interference detection and mitigation.
“We are excited for the opportunity to introduce our Vega 60 board,” said Miles Ware, director of marketing at Hemisphere. “Vega 60 brings our industry-leading heading and position solutions to an OEM board footprint with very few affordable upgrade paths.”
Electronic Warfare Kit enables dismounted soldiers to detect, map and mitigate the impact of navigational warfare (NAVWAR) attacks
TRX Systems, developer of NEON GPS-denied location solutions, has delivered the TRX Systems Dismount Electronic Warfare (EW) Kit prototype to the U.S. Army.
Developed for U.S. Army Rapid Capabilities and Critical Technologies Office (RCCTO), the TRX EW Kit is designed to extend EW and signal intelligence for the dismounted warfighter.
The kit adds powerful new capabilities to the company’s NEON Personnel Tracker-MIL solution, expanding the integration between its NEON Location Service and ATAK application to better equip dismounted personnel for detection and mapping of jamming and spoofing attacks.
New NEON functionality includes:
Robust Interference Detection. Rapidly detects and geo-references NAVWAR threats including GPS jamming, repeating and spoofing.
NAVWAR Threat Mapping. Increases situational awareness by geo-referencing and mapping detected threats through integration with ATAK and EW platforms.
Reliable Dismount Location Data. Mitigates the impact of NAVWAR attacks by eliminating erroneous GPS inputs while continuing to deliver reliable location data to dismounted users.
Integration with NAVWAR Devices. Integrates threat data from Orolia Defense & Security BroadSense Nano and other devices already carried by warfighters to provide a fused NAVWAR threat indication.
The NEON Personnel Tracker Military (PT-MIL) uses a suite of patented algorithms that fuse GNSS, an inertial sensor, ultra-wideband (UWB) and other inputs to deliver reliable position data to dismounted personnel operating in the presence of compromised or intentionally denied GNSS signals.
With the new EW Kit functionality, warfighters will receive real-time situational awareness into jamming or spoofing threats at their immediate location and from other dismount personnel sharing data over the TAK network.
The EW Kit is integrated via soldier plug-ins, enabling threats discovered and mapped by dismounts to be fused into the overall NAVWAR threat picture.
“In today’s conflict zones, it’s becoming increasingly easy for adversaries to launch electronic attacks against GNSS systems using low-cost jammers built with readily available commercial technology,” said Carol Politi, president and CEO of TRX Systems. “The EW Kit developed in the RCCTO program provides dismount soldiers with clear insight into their NAVWAR environment by rapidly detecting and characterizing these NAVWAR attacks, and it mitigates the impact by eliminating compromised data from their position solution.”
By Danny Baird NASA’s Space Communications and Navigation program office
The Artemis generation of lunar explorers will establish a sustained human presence on the Moon, prospecting for resources, making revolutionary discoveries and proving technologies key to future deep space exploration.
To support these ambitions, NASA navigation engineers from the Space Communications and Navigation (SCaN) program are developing a navigation architecture that will provide accurate and robust position, navigation and timing (PNT) services for the Artemis missions. GNSS signals will be one component of that architecture. GNSS use in high-Earth orbit and in lunar space will improve timing, enable precise and responsive maneuvers, reduce costs, and even allow for autonomous, onboard orbit and trajectory determination.
On Earth, GNSS signals enable navigation and provide precise timing in critical applications like banking, financial transactions, power grids, cellular networks, telecommunications and more. In space, spacecraft can use these signals to determine their location, velocity and time, which is critical to mission operations.
“We’re expanding the ways we use GNSS signals in space,” said SCaN Deputy Director for Policy and Strategic Communications J.J. Miller, who coordinates PNT activities across the agency. “This will empower NASA as the agency plans human exploration of the Moon as part of the Artemis program.”
Spacecraft near Earth have long relied on GNSS signals for PNT data. Spacecraft in low-Earth orbit below about 1,800 miles (3,000 km) in altitude can calculate their location using GNSS signals just as users on the ground might use their phones to navigate.
This provides enormous benefits to these missions, allowing many satellites the autonomy to react and respond to unforeseen events in real time, ensuring the safety of the mission. GNSS receivers can also negate the need for an expensive onboard clock and simplifies ground operations, both of which can save missions money. Additionally, GNSS accuracy can help missions take precise measurements from space.
Expanding the Space Service Volume
This photograph of a nearly full Moon was taken from the Apollo 8 spacecraft at a point above 70 degrees east longitude. Mare Crisium, the circular, dark-colored area near the center, is near the eastern edge of the Moon as viewed from Earth. (Image: NASA)
Beyond 1,800 miles in altitude, navigation with GNSS becomes more challenging. This expanse of space is called the Space Service Volume, which extends from 1,800 miles up to about 22,000 miles (36,000 km), or geosynchronous orbit. At altitudes beyond the GNSS constellations themselves users must begin to rely on signals received from the opposite side of the Earth.
From the opposite side of the globe, Earth blocks much of the GNSS signals, so spacecraft in the Space Service Volume must instead “listen” for signals that extend out over the Earth. These signals extend out at an angle from GNSS antennas.
Formally, GNSS reception in the Space Service Volume relies on signals received within about 26 degrees from the antennas’ strongest signal. However, NASA has had marked success using weaker GNSS side lobe signals — which extend out at an even greater angle from the antennas — for navigation in and beyond the Space Service Volume.
Since the 1990s, NASA engineers have worked to understand the capabilities of these side lobes. In preparation for launch of the first Geostationary Operational Environmental Satellite-R weather satellite in 2016, NASA endeavored to better document side lobes’ strength and nature to determine if the satellite could meet its PNT requirements.
“Through early on-orbit measurement and documentation of the GNSS side lobe capabilities, future missions could rest assured that their PNT needs would be met,” said Frank Bauer, who began the GNSS PNT effort at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. “Our understanding of these signal patterns revealed a host of potential new GNSS applications.”
Navigation experts at Goddard reverse-engineered the characteristics of the antennas on GPS satellites by observing the signals from space. By studying the signals satellites received from GPS side lobes, engineers pieced together their structure and strength. Using this data, they developed detailed models of the radiation patterns of GPS satellites in an effort called the GPS Antenna Characterization Experiment.
While documenting these characteristics, NASA explored the feasibility of using side lobe signals for navigation well outside what had been considered the Space Service Volume and in lunar space. In recent years, the Magnetospheric Multiscale Mission (MMS) has even successfully determined its position using GPS signals at distances nearly halfway to the Moon.
A graphic detailing the different areas of GNSS coverage. (Image: NASA)
GNSS at the Moon
To build on the success of MMS, NASA navigation engineers have been simulating GNSS signal availability near the Moon. Their research indicates that these GNSS signals can play a critical role in NASA’s ambitious lunar exploration initiatives, providing unprecedented accuracy and precision.
“Our simulations show that GPS can be extended to lunar distances by simply augmenting existing high-altitude GPS navigation systems with higher-gain antennas on user spacecraft,” said NASA navigation engineer Ben Ashman. “GPS and GNSS could play an important role in the upcoming Artemis missions from launch through lunar surface operations.”
While MMS relied solely on GPS, NASA is working toward an interoperable approach that would allow lunar missions to take advantage of multiple constellations at once. Spacecraft near Earth receive enough signals from a single PNT constellation to calculate their location. However, at lunar distances GNSS signals are less numerous. Simulations show that using signals from multiple constellations would improve missions’ ability to calculate their location consistently.
To prove and test this capability at the Moon, NASA is planning the Lunar GNSS Receiver Experiment (LuGRE), developed in partnership with the Italian Space Agency. LuGRE will fly on one of NASA’s Commercial Lunar Payload Services missions. These missions rely on U.S. companies to deliver lunar payloads that advance science and exploration technologies.
NASA plans to land LuGRE on the Moon’s Mare Crisium basin in 2023. There, LuGRE is expected to obtain the first GNSS fix on the lunar surface. LuGRE will receive signals from both GPS and Galileo, the GNSS operated by the European Union. The data gathered will be used to develop operational lunar GNSS systems for future missions to the Moon.
The billions of interconnected devices and sensors embedded in other devices, vehicles and even humans that collectively constitute the much-heralded internet of things (IoT) collect and share data used in myriad applications. This requires them to know their location, which is a challenge in GPS-denied environments, such as most indoor locations, tunnels and urban canyons.
A new approach helps networks of smart devices cooperate to find and communicate their positions in such environments. This “localization of things” could be helpful in applications ranging from autonomous vehicles to asset tracking, from supply-chain monitoring to smart cities and real-time mapping.
Traditional network localization methods estimate a single value for each geospatial variable, such as the distance between two nodes. Therefore, accuracy drops sharply in environments where multipath, a limited view of the sky, and other problems severely degrade GNSS and wireless signals. A paper by researchers at four institutions outlines a system to capture location information even in these challenging environments by fusing positional data of various kinds as well as digital maps.
The new method fuses data from various sensing measurements — such as radio, optical and inertial signals — and analyzes features of each signal — including its power, angle of arrival, and time of flight. It uses machine-learning techniques to weigh this “soft information” — the researchers call it that because their method does not favor any single “hard” number — to create a probability distribution of distances, angles and other metrics.
It also exploits contextual information from digital maps, dynamic models and node profiles to verify what is possible. For example, two nodes could not be 20 meters apart if they are both in an area with a maximum dimension of 10 meters.
To reduce the complexity and size of the data that it must collect to function, the new method identifies the most and least useful aspects of the received waveforms for the purpose at hand on the basis of a “principal component analysis.”
In simulations of challenging scenarios, full of reflections and echoes, the new system’s performance significantly surpassed that of traditional ones and consistently approached the theoretical limit for localization accuracy, while the accuracy of traditional systems dropped dramatically.
Evolution in civil aviation foresees a greater role for GNSS in onboard navigation systems as opposed to traditional terrestrial navigation aids. This will require improvements in managing the threat posed by RF interference.
Fortunately, the availability of more GNSS constellations and two carrier frequencies now enables GNSS equipment used aboard civil aircraft to not only detect and monitor spoofing, but also determine from which direction it is coming.
A recent paper details a procedure to do this. It consists of a detection module that employs an algorithm to identify which signals tracked by the receiver are counterfeit, if any, followed by a direction-finding module that implements an efficient direction-of-arrival (DOA) estimator. The procedure requires three GNSS antennas and the same number of receivers, time-synchronized with a common clock, plus a signal processor that implements the detection and DOA estimation algorithms. The paper presents the design of the chain of algorithms and their preliminary tests in a laboratory setup, with the simulation of several spoofing attacks, assumed realistic in a civil aviation scenario.
Citation: “An Algorithm for Finding the Direction of Arrival of Counterfeit GNSS Signals on a Civil Aircraft” by G. Falco, M. Nicola, E. Falletti and M. Pini, presented on Sept. 20, 2019, at the ION GNSS+ conference in Miami, Florida.
Joint Galileo/GPS Project on the ISS
The European Space Agency (ESA) and NASA conducted a joint Galileo/GPS space receiver experiment aboard the International Space Station (ISS). The objectives of the project were to demonstrate the robustness of a combined Galileo/GPS waveform uploaded to NASA hardware already operating in the challenging space environment — the SCaN (Space Communications and Navigation) software defined radio (SDR) testbed (FPGA) — on-board the ISS.
The activities included the analysis of the Galileo/GPS signal and on-board position/velocity/time (PVT) performance, processing of the Galileo/GPS raw data (code and carrier phase) for precise orbit determination, and validation of the added value of a space-borne dual GNSS receiver compared to a single-system GNSS receiver operating under the same conditions. A recent paper provides a general overview of the experiment (called GARISS) and describes its design, test, validation, and operations. It also presents the various analyses conducted in the context of this project and the results obtained, with a focus on the (Precise) Orbit Determination results.
Citation: “The joint ESA/NASA Galileo/GPS Receiver onboard the ISS – The GARISS Project” by W. Enderle, E. Schönemann, F. Gini, M. Otten, P. Giordano, J. Miller, S. Sands, D. Chelmins, O. Pozzobon, presented on September 20, 2019, at the ION GNSS+ conference in Miami, FL.
Recently launched satellites of BeiDou Phase 3 program have started broadcasting new signals. Javad GNSS announced successful tracking of these signals and provided the adjacent figures.
Interface control documents (ICDs) for B1C and B2A signals are available, while an ICD for the other signal, called B2B, has not yet been published. The company tracked the signal on the 1207.14 Mhz frequency on BeiDou’s satellites 32, 33 and 34, and subsequently saw that this signal is available on all recently launched BeiDou Phase 3 satellites, and tracked it successfully.
This B2B signal plus B2A signal together form an AltBOC(10,15) signal on 1191.795 MHz — JAVAD GNSS calls it BaltBOC. Assuming that BOC parameters of this signal are similar to Galileo’s, the company tracked this. Figures 1 and 2 show BeiDou andGalileo (BaltBOC and altBOC) discriminator curves; they appear identical.
Figure 1. BeiDou AltBoC signal. Red and blue: I of B2A(E5A) and B2B(E5b) sub-signals; purple and yellow: Q of B2A(E5A) and B2B(E5b) sub-signals (their sum is zero); green and aqua: I (early-minus-late) of B2A(E5A) and B2B(E5b) sub-signals. (Chart: Javad GNSS)Figure 2. Galileo AltBoC signal. Colors same as Figure 1. (Chart: Javad GNSS)
According to another source, the signals are mentioned in some publications (Figure 3, 4 and 5 from an official Chinese government presentation at the International GNSS Service Workshop, Oct. 2018) and intended to be open signals, but an ICD is presently missing. However there appears to be some clarity now, that the modulation of B2a+b is an “ACE-BOC” modulation, which is similar to but formally different from “AltBOC.”
Figure 3. BDS-3 demonstration constellation. (Chart: Javad GNSS)Figure 4. Signals of test system BDS-3. (Chart: Javad GNSS)Figure 5. BDS-3 signal modulations. (Chart: Javad GNSS)
Detection of anomalous harmonics in the L1 spectrum
Interfering signals are one of the most well-known nuisance for GNSS receivers. A number of terrestrial systems and devices can generate various types of interference, either intentionally or not, but one would not expect interfering signals to arrive from space. On May 17, researchers of the Navigation Signal Analysis and Simulation (NavSAS) Group at the Politecnico di Torino detected the presence of anomalous spikes in the L1 signal spectrum. The origin of the spikes was identified to be the transmission of non-standard codes from a non-operational GPS satellite (GPS IIF-9, SVN49). In this article, we report on some of the most significant signal observations we performed in an effort to identify and localize the source of the interference and we address the possible impact it could have on GNSS signal processing.
By Fabio Dovis, Nicola Linty, Mattia Berardo, Calogero Cristodaro, Alex Minetto, Lam Nguyen Hong, Marco Pini, Gianluca Falco, Emanuela Falletti, Davide Margaria, Gianluca Marucco, Beatrice Motella, Mario Nicola and Micaela Troglia Gamba
On the afternoon of May 17, 2017, during an outdoor data collection experiment, researchers of the NavSAS Group detected the presence of two spikes in the L1 spectrum, with sufficient power to be clearly visible on a display of the spectrum obtained by processing the raw digital samples at the receiver’s intermediate frequency. The initial check looked for a possible interfering source in the experimental set-up, since it was quite complex and included multiple GNSS receivers, PCs, a video camera and a couple of car batteries. But the likelihood of this source was soon dispelled as the same kind of spectrum was visible on a spectrum analyzer (SA) connected to an active, survey-grade GNSS antenna mounted on the lab roof, as displayed in FIGURE 1. The spectrum is centered at 1575.42 MHz, with the SA set to a frequency span of 5 MHz. Connecting the SA to a different survey-grade antennas on the lab roof, we saw no remarkable differences.
The spikes also appeared on subsequent days, becoming clearly visible at about 13:00 UTC and disappearing at about 19:00 UTC, as illustrated in FIGURE 2. The main lobe of the GPS signal spectrum is visible, along with two spikes, at approximately ±0.5 MHz above and below the L1 carrier frequency. Weaker harmonics are also visible at ±1.5 MHz from the central frequency.
Figure 1. L1 Spectrum of the received signal at 16:51 (Central European Summer Time; 14:51 UTC) on May 19, 2017, at the NavSAS Lab, Torino (located at 45°03’54.98767″ N, 7°39’32.28920″ E, 311.9667 meters).Figure 2. Spectrogram of the received signal. Power spectral density (PSD) is color coded.
Response from the U.S. Air Force about the anomaly
The 2nd Space Operations Squadron is performing maintenance on a presently non-operational satellite. SVN49 is broadcasting non-standard C/A and non-standard Y codes as described in IS-GPS-200. Space professionals continue to conduct safe and responsible command and control of the constellation to continue to provide accuracy that exceeds established system requirements.
As always, GPS users who experience issues should address them through the appropriate channels: military users should contact DSN 560-2541, commercial 719-567-2541 while civilian users should contact the U.S. Coast Guard Navigation Center at 703-313-5900.
Very Respectfully,
NICHOLAS J. MERCURIO, Capt, USAF Director, 14th Air Force (Air Forces Strategic)/JFCC SPACE Public Affairs
Exclusion of terrestrial sources
The 24-hour repetition period of the phenomenon, along with the shape of the spectrum, could indicate the presence of a signal anomaly from a GNSS satellite. However, we could not exclude the hypothesis of unintentional interference generated by a nearby terrestrial communication system, since the area is crowded with research labs belonging to the Instituto Superiore Mario Boella and the Department of Electronics and Telecommunications of Politecnico di Torino. Nevertheless, we probed the L1 spectrum in a wider area using a simple setup, consisting of a patch antenna and a narrow-band front end. We analyzed the spectrum at the output of the front-end’s analog-to-digital converter, plotting the results on a smartphone running our software receiver in real time.
FIGURE 3 shows the L1 spectrum observed several kilometers from the NavSAS Lab. The shape of the spectrum is different than that in Figure 1 because of the narrow-band filter of the front end, but again, the presence of the two spikes is clearly visible at ±0.5 MHz from the central frequency, approximately with the same power strength. In addition, during a dynamic data collection experiment, we recognized that the interfering signals disappeared when the western part of the sky was obscured by buildings, as demonstrated in Figure 3. This was further investigated (and confirmed) when we processed the collected set of data in the lab. At that time (May 19), the hypothesis of an interfering signal from space became more plausible.
Figure 3. L1 Spectrum of the received signal observed on the afternoon of May 19 in Torino, 6.7 kilometers away from the NavSAS Lab: (left) in open sky conditions, (right) with the western portion of the sky obscured by a nearby building.
Meanwhile, the presence of suspicious spikes was confirmed by colleagues at the European Commission Joint Research Centre located in Ispra, Italy, and also from researchers of the Finnish Geodetic Institute in Helsinki, Finland, and by the South African National Space Agency at the station of the South African National Antarctic Expedition IV. These multiple observations definitely excluded the possibility that the interference it could be coming from terrestrial sources or from within the receiving equipment.
Checking the satellites in view during the presence of the spikes in the spectrum (that is, from about 13:00 to about 19:00 UTC) and bearing in mind the periodicity of the event over consecutive days, we excluded the possibility that a Galileo satellite could be the source of interference. It is indeed known that, due to an orbital period of approximately 14 hours for observers on the ground, the constellation geometry repeats only every 10 days.
Figure 4. Visible operational GPS, Galileo and BeiDou satellites over Turin for the full time window between 13:00 and 19:00 UTC on May 20, 2017.
FIGURE 4 shows the visibility of operational satellites over the full time window of interest for the GPS, Galileo and BeiDou constellations.
Considering the duration of the satellites’ visibility, the search for the source of interference was restricted to SVN71 (PRN26), SVN45 (PRN21) and the C11 BeiDou satellite. However, considering the previous tests, the satellite should have been in the western portion of the sky with respect to our location, and the only operational satellite of this set is SVN71, which we initially identified as the possible source of the interfering signal.
GPS SVN71 (PRN 26) or SVN 49?
The frequency of the harmonics could be measured over time. The first peak at approximately 0.5 MHz above the central frequency was analyzed by post-processing a set of digital samples collected with an Universal Software Radio Peripheral, which was slaved to a 10-MHz rubidium standard and which converted the RF signal to baseband, sampling it at 5 MHz. The frequency was measured exploiting a Welch periodogram, based on a 102,400-point discrete Fourier transform, with rectangular windowing and no window overlaps.
FIGURE 5 (a) shows the trend of the measured frequency versus time, from 12:43 to 18:38 UTC, on May 21. The frequency profile reveals that it is not constant and has a trend similar to the typical Doppler frequency shift of a GPS satellite. FIGURE 5 (b) shows the derivative of the frequency, with a minimum around 16:22 UTC. At that time, we expected to have a null Doppler shift from GPS PRN26, whereas the frequency of the peak was equal to 510.449 kHz. This is the inverse of 1.959056 microseconds, which is close to the inverse of twice the chip length, 2/Rc = 1.955034 microseconds. This indicates that the interfering signal could be a square wave with the same rate as the C/A spreading code.
Figure 5(a). Measured frequency of the first upper harmonic versus time.Figure 5(b). Measured frequency of the first upper harmonic versus corresponding frequency rate.
FIGURE 6 shows the Doppler frequency of PRN26 (blue line), as estimated by the tracking loop of a GNSS software receiver, and compares the Doppler shift to the frequency of the first upper peak (orange line), measured on the spectrum. It is possible to note that the two curves almost overlap, with a significant difference at the beginning and at the end of the observation. Thus, although the frequency of the peak follows the Doppler trend of a GPS satellite, it does not exactly match the Doppler curve of PRN26. This result weakened the hypothesis indicating that PRN26 was the source of the interference.
Furthermore, since it was still possible to acquire and track the L1 C/A-code signal from PRN26, this satellite was unlikely to be the source of the interfering components. Thus, also motivated by the mismatch in the Doppler shift of PRN26 (as previously highlighted in Figure 6), we started to think that the source of the interference could be another satellite broadcasting a GPS-like signal.
The search then focused on potential sources of interference coming from a non-operational satellite. The non-operational GPS satellite SVN49, launched on March 24, 2009 (also known as NAVSTAR 63 with NORAD ID 34661), has an orbit similar to that of SVN71 (see FIGURE 7). The previous remarks, let us guess that the transmission of a non-standard code (NSC) from such a satellite was the origin of the problem in the L1 spectrum. Such a case, could be similar to what has been previously reported in by Zhu et al. [1,2] when discussing the effects of the transmission of an NSC on Nov. 28, 2006.
Figure 6. Doppler shift of GPS PRN26 estimated by a tracking loop (blue line) and comparison with the measured frequency of the first upper harmonic versus time (orange line).Figure 7. Skyplot illustrating the path of SVN71 (PRN26) and SVN49 over the time window of interest.
Transmission of NSCs for testing purposes is foreseen in the GPS Interface Specification, IS-GPS-200 [3]. GPS satellites can switch off regular broadcasts of the C/A code and the P/Y code and transmit a non-standard C/A code and non-standard Y code. Such operation is intended to protect users from receiving and utilizing erroneous satellite signals in case of unhealthy conditions on the spacecraft. Strictly speaking, this case cannot be formally considered as an “anomaly,” because the transmission of non-standard codes is documented in the IS-GPS-200. Therefore, the transmission of an NSC can be considered a normal operation in itself, even though it may reflect a problem with the transmitting satellite.
However, in this case the choice of the spreading sequence, which is likely a square wave, allowed the total power of the signal to be concentrated in just a few spectral components, thus originating continuous-wave-like in-band signals.
The distribution of the harmonics, the main components of which are at ±500 kHz, and the presence of the odd harmonics only, matches the case recalled by Zhu et al. [1,2], of a transmission of an NSC modulated as a binary-phase-shift-keying (BPSK) sequence with alternating logical 0s and 1s, transmitted at the C/A code chipping rate (Rc=1.023 megachips per second). The spectrum of this “square wave” with period used as a spreading signal is in fact know to be (1)
where δ is the Dirac-δ function. Zhu et al. [1,2] considered this specific case of a “non-standard code” to be especially remarkable, because it can affect the L1 spectrum, introducing multiple harmonic components similar to those previously illustrated in Figure 1 and Figure 3 (a).
Figure 8. Spectrum of the simulated NSC for different C/N0 values.
The hypothesis of the BPSK with Rc=1.023 megachips per second spreading signal has been verified by simulation. Figure 8. shows how the tested case of a received signal from SVN49 with a C/N0=55 dB-Hz best matches the measured spectrum when SVN49 is at its maximum elevation angle and the power of the received signal is the strongest.
However, it has to be remarked that according to Zhu et al. [1,2], the NSC is designed to have negligible effect on tracking other healthy GPS satellite signals. Nonetheless, their analyses showed that an NSC transmission (as occurred on Nov. 28, 2006) can have a non-negligible impact in the performance on user equipment. In detail, when a GPS satellite is switched to NSC mode, a receiver immediately loses its capability to track that satellite signal. This is not the case with SVN49 as it is currently declared non-operational. However, due to the modified code sequence, an even worse effect is possible. In fact, the NSC introduces irregular components at a sustained level in the GPS signal spectrum.
As a final confirmation of the transmission of the NSC from SVN49, we have used the technique of averaging and summing over the code period as described by Mitelman [6]. Considering a time window during which the Doppler shift of the signal is negligible, we have extracted the spreading code, confirming the square wave hypothesis (see FIGURE 9).
Figure 9. Square wave code obtained by averaging and summing.
According to the Notice Advisory to Navstar Users (NANU) 2001701, SVN49 was broadcasting standard signals as PRN04 (although set unhealthy) since the beginning of the year, but NANU 2017042 announced that PRN04 was to be re-allocated to SVN38 starting from May 18. This switch actually matches the dates when we started to see the spikes in the spectrum, since, probably, the SVN49 started that day to use the “square wave” for the spreading.
Implementing the square wave local code, it has been possible to successfully acquire and track the NSC, as shown in FIGURE 10.
Figure 10. Acquisition and tracking of the NSC. Source: GPS World
Source: GPS World
The real-time software receiver N-Gene, documented by Molino et al. [5],has been forced to acquire and track in real time the signal coming from SVN49. FIGURE 11 shows a screenshot of the N-Gene graphical interface while processing this signal.
Figure 11. N-Gene software receiver processing the SVN49 signal.
The receiver was able to perform the decoding of the navigation message transmitted by SVN49, which exhibits a regular format, even if marked with an unhealthy flag (see FIGURE 12).
Figure 12. Decoded navigation message.
Impact on receiver signal processing
It is well known that the spectrum of GNSS signals is basically a line spectrum in the frequency domain, which is susceptible to interference (see, for example, the book edited by Davis [4]).
Interference with harmonic components such as those generated by the use of a square wave could strongly impact a GNSS receiver in the acquisition and tracking blocks because the interference power is dispersed over the whole search space by the correlation with the local code, compromising the acquisition accuracy and impacting other functional blocks. The impact of interference spectral lines strongly depends on their location within the frequency band. This is due to the almost periodic nature of the GNSS signals. In fact, the spectrum of a GNSS signal has components spaced at multiples of the inverse of the code period (for example, 1 kHz for GPS C/A code) with different power allocated to each component depending on the shape of the code spectrum. The effect is larger in case of matching of the interference spectral components with the ones of the GNSS signal. Furthermore, in the present case, the strongest harmonics are close to the L1 carrier frequency and are not mitigated by the front-end filter since they fall within its narrow bandwidth.
As opposed to the case discussed by Zhu et al. [1,2] when GPS was virtually the only code-division-multiple-access system occupying the bandwidth around L1, the overall GNSS scenario has changed a lot recently. Galileo and BeiDou are also present, and the signals of the Galileo system, due to the different structure and code periods, have spectral lines spaced at 0.25 kHz. The frequency modulation of the interfering signal due to the variable Doppler shift is then even more likely to hit some of the spectral components of these signals.
We are performing further investigations are being performed to assess the impact of the interfering signal from SVN49 on Galileo-based high accuracy applications.
Acknowledgments
The NavSAS Group thanks Dr. Matteo Paonni (EC Joint Research Centre) for the support given in the analysis of the L1 signal spectrum and Dr. Laura Ruotsalainen (Finnish Geospatial Institute) and Danielle Taljaard (South African National Space Agency), who performed the data collection in Antarctica.
Bios
Fabio Dovis, Nicola Linty, Mattia Berardo, Calogero Cristodaro, Alex Minetto and Lam Nguyen Hong are with the Navigation Signal Analysis and Simulation (NavSAS) Group, Politecnico di Torino, Torino, Italy.
Marco Pini, Gianluca Falco, Emanuela Falletti, Davide Margaria, Gianluca Marucco, Beatrice Motella, Mario Nicola and Micaela Troglia Gamba are with the Navigation Technologies Research Area of Istituto Superiore Mario Boella, Torino.
[2] “Satellite Anomaly and Interference Detection Using the GPS Anomalous Event Monitor” by Z. Zhu, S. Gunawardena, M. Uijt de Haag and F. van Graas in Proceedings of the 63rd Annual Meeting of The Institute of Navigation, Cambridge, Massachusetts, April 23–25, 2007, pp. 389–396.
[4] GNSS Interference Threats and Countermeasures by F. Dovis (ed.) published by Artech House, Norwood, Massachusetts, 2015.
[5] “N-Gene GNSS Software Receiver for Acquisition and Tracking Algorithms Validation” by A. Molino, M. Nicola, M. Pini and M. Fantino in Proceedings of EUSIPCO 2009, the 17th European Signal Processing Conference, Glasgow, Scotland, Aug. 24–28, 2009, pp. 2171-2175.
José Ángel Ávila Rodríguez (left)) and Laurent Lestarquit holding a satellite model. (Credit: ESA)
The engineering team behind the signal technology underpinning Europe’s Galileo satellite navigation system has reached the final of this year’s European Inventor Award, run by the European Patent Office, reported the European Space Agency.
The team is led by Spanish engineer José Ángel Ávila Rodríguez — now part of ESA’s Galileo team — and his French colleague Laurent Lestarquit from France’s CNES space agency.
The team also includes German Günter Hein, formerly head of the department studying the evolution of EGNOS and Galileo for ESA, as well as Belgian Engineer Lionel Ries, now in ESA’s technical directorate, as well as French CNES engineer Jean-Luc Issler.
The engineers, who had previously worked together as members of the multinational Galileo Signal Task Force, came up with a pair of innovative signal modulation techniques to pack multiple Galileo signals together, simultaneously serving different sets of users while boosting signal performance and robustness. Both innovations have been adopted by Galileo and are in use today.
The first technique, called Alternative Binary Offset Carrier modulation — AltBOC — combines four signals into one large one, resulting in the widest bandwidth navigation signal ever transmitted. Two of these signals are sitting on the one carrier, namely E5a, while the other two are on E5b.
“AltBOC is a way of transmitting four components in a very wide bandwidth signal, using a single radio frequency chain on the satellite in an intelligent way, where originally two chains would have been needed to transmit in two separate frequency bands (E5a and E5b),” explains José Ángel, now ESA’s global navigation satellite system evolution signal and security principal engineer for Galileo.
“The result is a frequency-rich signal that fundamentally improves positioning performance and robustness.
“AltBOC is interoperable with GPS in E5a/L5 and allows receiver manufacturers to process it as one very large signal – extending over the whole E5a and E5b range – or as two separate signals, one at each frequency carrier (E5a or E5b).
“AltBOC serves open service users in general. Moreover, when used in its full performance mode (E5a+E5b), it also facilitates geodetic and precision scientific applications such as gradual tectonic motion, or the use of accurate positioning on Earth — including proposed ‘reflectometry’ missions to make altimetry measurements from satnav signals reflected from Earth’s surface.
“But the application of AltBOC could go beyond the current use by providing accurate positioning to satellites in space thanks to its unique bandwidth characteristics.”
