SeRo Systems, a German-based leader in air traffic surveillance security and monitoring solutions, is expanding its portfolio with the launch of its newest monitoring technology for improved aircraft situational awareness. The live GNSS RFI Situation Display (GRSD) is a real-time solution that combines live air traffic information with SeRo’s advanced GPS jamming and spoofing detection and short-term predictive alerts — offering enhanced visibility into the airspace.
For more than 10 years, SeRo has developed advanced air surveillance and monitoring technology for customers including EuroControl, Baltic air navigation service providers (ANSPs) and spectrum regulators, Austro Control, armasuisse and other aviation organizations. SeRo is the only company that provides real-time GNSS RFI monitoring to two of the three Baltic states.
Operational picture at a glance
Designed with and customized for ANSPs and spectrum regulators, the new GRSD leverages SeRo’s vertically integrated receiver network and uses its anomaly detection and high-precision multilateration (MLAT) to help users assess their operational picture at a glance. The system monitors the airspace and displays live traffic combined with a color-coded real-time GNSS interference intensity map that identifies zones subject to interference.
Its short-term interference alerting feature utilizes AI to predict when aircraft will experience interference and gives the user a time estimate. As soon as an aircraft is impacted by spoofing, GRSD automatically highlights the aircraft and generates an alert indicating both the spoofed and the correct aircraft position.
“With jamming and spoofing incidents on the rise, timely and actionable intelligence matters more than ever,” said Matthias Schäfer, CEO of SeRo Systems. “Our new GRSD product delivers real-time insights on GNSS RFI and provides a live operational view that helps users prepare and respond.
GRSD works seamlessly alongside SeRo’s SecureTrack platform, combining real-time data for instant decision-making with historical insights for strategic airspace monitoring, analysis, reporting, and incident investigation. “Together with our SecureTrack solution, ANSPs and spectrum regulators now have the tools they need for unmatched situational awareness,” Schäfer said.
The National Geospatial-Intelligence Agency (NGA) is seeking information from the GNSS community on upgrades to its Stardust program.
Stardust develops models of the Earth used in geomatics. The upgrades will result in modernization of geomatics information technology systems and infrastructure. The update includes migration of models to the cloud.
Stardust is run by the NGA Foundation GEOINT Integrated Program Office, partnered with the Foundation GEOINT Group (NGA/SF) within the Source Operations and Management Directorate.
AS CAT STEVENS (yes, he’s back to using his old name) famously sang on “Wild World”:
“… take good care Hope you make a lot of nice friends out there But just remember there’s a lot of bad and beware Beware.”
While he was talking about a girlfriend leaving him, the warning can just as well apply to GNSS users — especially those relying on GNSS for safety-of-life navigation and the maintenance of critical public infrastructure systems.
GNSS signals are relatively weak and they are susceptible to unintentional and intentional jamming that can make reception of the signals difficult or impossible. The jamming of radio signals to hinder reception is nothing new. It’s been used by those wanting to interfere with the use of the radio spectrum ever since radio became an important tool for communication and navigation in the early 20th century. Jamming has been used in hot wars to try to defeat military communication as well as in cold wars to try to prevent a perceived enemy from broadcasting to a particular country’s citizens. Notably, the shortwave radio broadcasts from Western countries were jammed by the former Soviet Union. And even today, broadcasts directed at China, Cuba and some other countries are regularly jammed.
GNSS is also being intentionally jammed on a regular basis in some parts of the world for various purposes including the protection of politicians and civilian infrastructure and to foil GNSS-guided munitions. But while directed at supposed threats, the jamming affects all GNSS receivers in a certain radius of the jammer. Such jamming activities are being reported in the popular press with an increasing frequency.
While GNSS jamming is receiving increased attention in our troubled world, even more pernicious is GNSS spoofing. Spoofing is the attempt to mimic GNSS signals to try to trick a receiver into tracking them and thereby compute a wrong position and/or time at the receiver. This can have disastrous consequences if not detected immediately and the use of GNSS deactivated.
So, how do you detect GNSS signal jamming and spoofing? We have discussed this issue in several columns over the years, but in this month’s column, a team of researchers from Stanford University and the University of Colorado describe how they are using relatively inexpensive equipment and sophisticated software and analyses to detect and warn of GNSS jamming and spoofing. Clearly, they are heeding Cat Stevens’ warning.
By Leila Taleghani, Fabian Rothmaier, Yu-Hsuan Chen, Sherman Lo, Todd Walter, Dennis Akos and Benon Granite Gattis
GNSS signals are extremely low power by the time they reach users on Earth and are easily overwhelmed by nearby terrestrial signals. Such signals can interfere with a user’s ability to receive the desired GNSS signals or, even worse, replace them with simulated signals that cause the user to obtain the wrong position or time estimate. Two major types of radio-frequency interference (RFI) threats have been identified: jamming and spoofing. Jamming results from emissions that do not mimic GNSS signals, but interfere with the receiver’s ability to acquire and track GNSS signals. Spoofing is the emission of GNSS-like signals that may be acquired and tracked in combination with, or instead of, the intended signals.
Both threats have been studied at length by researchers, and their presence around the globe has been reported even in the popular press. Some research has been done into the prevalence of spoofing. Even so, there is no well-developed understanding of how widespread these threats are.
Terrestrial interfering signals may be fairly weak and only effective in a limited area. Complex environments with buildings or terrain may further limit their effective area of influence and hinder the ability of external interference detection. To create a better understanding of the presence and characteristics of jamming and even spoofing, we are developing a low-cost RFI detector based on a commercial, off-the-shelf GNSS receiver: the u-blox F9. We are pairing this receiver with a Raspberry Pi computer and are developing custom software to monitor the receiver outputs and store data surrounding interesting events.
We are developing a toolset in MATLAB and C/C++ with the intention of processing and analyzing the u-blox data. The toolset includes functionality to decode selected u-blox messages that contain parameters of interest. These metrics include automatic gain control (AGC), carrier-to-noise-density ratio (C/N0) and spectral power. They also include raw pseudoranges from multiple constellations and internal u-blox interference metrics. With the volume of data that can be gathered from continuous monitoring, we have begun characterizing nominal performance and developing approaches to spoofing and jamming detection. The publicly available code can be accessed through our Git Repository at https://github.com/stanford-gps-lab/navsu.
With the raw pseudoranges and downloaded broadcast ephemeris data, we compute navigation solutions using different combinations of constellations and frequencies. When the individual and multi-constellation position solutions are compared to each other, discrepancies can be flagged and investigated for possible interference. We have begun characterizing nominal power metrics such as AGC and C/N0. With the quantity of data that we can get from the RFI monitor, we are working to characterize other receiver-specific parameters such as the u-blox continuous wave (CW) jamming indicator. We leverage data collected under nominal and jammed conditions to understand and identify a threshold for what can be considered interference.
Many different methods have been proposed for GNSS interference detection and mitigation with large-scale data at multiple locations. In this article, we present our data-selection process, our development of thresholds for determining interference, and results from three u-blox receivers set up at different locations in the United States to glean information about nominal (non-spoofed) conditions. We inform our thresholds and analysis tools using datasets from nominal conditions, and then compare their performance to a dataset containing RFI events from a government-sanctioned jamming and spoofing test. Our results display how we leverage simple and powerful metrics informed by a low-cost receiver to understand nominal noise environments and successfully identify jamming and spoofing events.
Data and Metrics
We collect and analyze a variety of data types and metrics to help identify and characterize jamming and spoofing occurrences. The receiver model we started with, u-blox ZED-F9P-02B, can monitor two different RF bands and many signals, including GPS L1C/A, L2C; GLONASS L1OF, L2OF; Galileo E1B/C, E5b; BeiDou B1I, B2I; QZSS L1C/A, L1S, L2C; and SBAS L1C/A. It has 184 channels, which can be configured to sweep through an array of signals to be monitored. We are also developing monitors based on the recently released ZED-F9T-10B, which is capable of L1 and L5 signal reception. TABLE 1 describes which version of the u-blox receivers each dataset comes from.
TABLE 1. Locations of u-blox monitor for nominal noise environment characterization and jam/spoof test. (Data: Authors)
L1 and L5 are the primary frequencies used for aviation, hence a monitor for these frequencies would be more useful for protecting aviation than the F9P, which is only capable of L1 and L2 reception. The available data includes raw measurements such as code and carrier phase, position estimates, power level estimates including C/N0, AGC and spectral power. It also has active CW interference detection. These metrics are all necessary for the consistency checks and power monitoring methods we summarize in this article. Consult our conference proceedings paper for details (see Acknowledgments). By examining all of these signals and measurements, we can observe changes in the RF environment and detect inconsistencies in the received signals.
Data Logging. The u-blox receiver logs messages in a specific format. The message types important to log are selected based on the desired data. Due to limited bandwidth, we prioritized messages that efficiently include all desired parameters for the interference detection methods we describe in this article. We have used both the u-blox F9P and the u-blox F9T.
To characterize nominal noise environments, u-blox receivers were set up at three locations: Stanford University, the University of Colorado (CU) in Boulder, and at the Colorado Springs airport. All measurements from satellites below an elevation angle of 5 degrees were ignored. The results from these locations are summarized below. Results from a jamming/spoofing test sanctioned by the U.S. Department of Homeland Security are presented and labeled with the acronym “GET-CI” (GPS Testing for Critical Infrastructures) in the subsequent discussion. Table 1 describes the parameters of the u-blox receiver at each location.
Positioning Metrics Development.The nominal error of the single- and multi-constellation position solutions is made by noting the difference between the computed position and the known truth. The inter-constellation consistency check is defined as the difference between the positions computed from two constellations, with no reference to a known truth position. To analyze the nominal differences in the north, east and down (NED) directions, we use the position covariance matrix, R, computed in the least-squares solver, to set a covariance-bound threshold. The covariance for each constellation is assumed independent. We present our results using this threshold in our results sections.
Our results in FIGURE 1 show that the Galileo position solution variance is higher than the dual-constellation and GPS-only solution. This is attributed in part to the fact that Galileo, while operational, has not filled out all planned satellite slots and therefore has fewer satellites and worse geometry than GPS.
FIGURE 1a. Map visualization of the comparison among position solutions computed using only GPS, only Galileo and a combined GPS plus Galileo dual-constellation solution at Colorado Springs. (Image: Authors)
FIGURE 1b. Map visualization of the comparison among position solutions computed using only GPS, only Galileo and a combined GPS plus Galileo dual-constellation solution at CU Boulder. (Image: Authors)
FIGURE 1c. Map visualization of the comparison among position solutions computed using only GPS, only Galileo and a combined GPS plus Galileo dual-constellation solution at Stanford. (Image: Authors)
Nominal Noise Results
Here are some of our positioning and power monitoring results under nominal reception conditions.
Positioning. Based on the methods described earlier, we present a selection of our results from the positioning consistency checks. We present several informative visualizations of the error between the computed position solution and the known truth of each u-blox receiver and use the covariance threshold to bound the raw error. The error for dual-constellation, single-constellation and inter-constellation consistency checks are all displayed and compared to one another. The pseudorange residuals and their accompanying chi-squared (χ2) statistic are also evaluated and compared for the GPS and Galileo single-constellation position solutions.
Positioning Consistency Comparison Maps. From the maps in Figure 1, we observe that Galileo has the highest error, followed by GPS, and then the dual-constellation solution. The map also serves as a method to spatially visualize the tails of the error distribution.
NED Time Histories. We compare the time history of the dual-constellation, GPS and Galileo position solution error to the three sigma (3σ) covariance bound computed at each epoch (see FIGURE 2). We also compare the GPS vs. Galileo inter-constellation difference to the 3σ covariance bound. The covariance bound is never crossed, indicating that 3σ threshold is conservative for both the error and the inter-constellation difference between GPS and Galileo.
FIGURE 2a. Dual-constellation north-east-down error vs. known truth, bounded by a 3σ threshold, at Colorado Springs. (Image: Authors)
FIGURE 2b. Dual-constellation north-east-down error vs. known truth, bounded by a 3σ threshold, at CU Boulder. (Image: Authors)
FIGURE 2c. Dual-constellation north-east-down error vs. known truth, bounded by a 3σ threshold, at Stanford. (Image: Authors)
Pseudorange Residuals and χ2 Statistic Threshold. Pseudorange residuals have a long history of being used as a consistency check between range measurements. As an example, the pseudorange residuals for the GPS position solutions are shown in FIGURE 3, and their corresponding χ2 statistic is shown in FIGURE 4.
FIGURE 3a. GPS pseudorange residuals at Colorado Springs. (Image: Authors)
FIGURE 3b. GPS pseudorange residuals at CU Boulder. (Image: Authors)
FIGURE 3c. GPS pseudorange residuals at Stanford. (Image: Authors)
FIGURE 4a. GPS χ2 and probability of false alert (PFA) threshold for the nominal noise environments at Colorado Springs. (Image: Authors)
FIGURE 4b. GPS χ2 and probability of false alert (PFA) threshold for the nominal noise environments at CU Boulder. (Image: Authors)
FIGURE 4c. GPS χ2 and probability of false alert (PFA) threshold for the nominal noise environments at Stanford. (Image: Authors)
The χ2 statistic is computed using the finite pseudorange residuals at each epoch, where the degrees of freedom are n − 4, where n is the number of satellites used at that epoch and 4 is the number of variables solved for (x, y, z, and the receiver time offset) when using a single constellation. A p-value is computed using the cumulative distribution function (CDF) of the χ2 statistic, and indicates the probability that the χ2 statistic at each epoch would be greater than the observed value. The statistic is compared to a theoretical 10−9 probability of false alert (PFA) based on the theoretical χ2 and the actual degrees of freedom of each epoch. Very low values for the χ2 statistic, such as those obtained with Galileo, are attributed to regions where very few satellites are in view, thus decreasing the degrees of freedom. Any spikes in the pseudorange residuals are also reflected with a higher χ2 statistic and low p-value, though those residuals are de-weighted in the position solution and ultimately do not trigger the 10−9PFA threshold or the 3σ threshold, thus indicating that a 10−9PFA is a conservative threshold.
Power Monitoring. For each nominal location with a u-blox receiver, we analyze results from the power-monitoring metrics mentioned earlier. We also observe results from the internal u-blox jamming indicators in a region where a possible RFI event was observed.
For power monitoring, we analyze spectral power and programmable gain amplifier (PGA) results.
For the nominal noise environments, the spectral power, PGA and corresponding C/N0 results indicated no significant anomalies.
Threshold and Metric Validation Results
An examination of thresholds and other metrics are important for characterizing RFI.
GPS Testing for Critical Infrastructure. From a DHS-sanctioned RFI testing event, we identify five regions of interference or spoofing. To identify the interference, we use a combination of the power and positioning metrics as well as the thresholds we developed through the characterization of the nominal noise environments described in the previous sections of this article.
We use the thresholds and tests we’ve developed to identify regions of spoofing and RFI events (labeled C I1–C I5) in the GET-CI dataset. For ease of comparison, all regions are labeled on plots that display the full 5.5 hours of data collection. All details as to the truth location and time of the test have been removed. C I1 is identified through the power metrics. C I2–C I5 are identified as regions that the NED difference between GPS and Galileo clearly crossed the 3σ threshold in all three directions, as visualized in FIGURE 5.
FIGURE 5a. Map view of solutions using GPS, Galileo and GPS plus Galileo for the DHS-sanctioned RFI testing event (identifying coordinates and physical features removed). (Image: Authors)
FIGURE 5b. Corresponding log-scale visualization of the GPS vs. Galileo position solution difference in the north-east-down directions. (Image: Authors)
From our pseudorange residuals, it appears as though the most significant interference events happened on the GPS constellation, as indicated by the high pseudorange residuals that fall into the C I2 and C I5 regions. Using the GPS χ2 statistic and p-value computations, we determined that the regions that crossed the 10−9 PFA threshold line are consistent with the regions of interference identified in Figure 5. The Galileo χ2 statistic, p-values and pseudorange residuals all show signs of possible interference. These regions are explored more in the power monitoring discussion below.
Since the GPS pseudorange residuals and χ2 statistic results show more signs of spoofing than the Galileo ones, we explore the Galileo-only position solution. Because the truth position is unknown, we take a point during the non-C I regions and define this as the “truth,” that is, a point in the position solution we believe has not been subject to spoofing. Any references to a truth position are from a position recognized as “truth” through post-processing rather than from a pre-determined and known location.
The p-values dip in each of the C I regions, but are lowest in regions C I5. Combined with the fact that the pseudorange residuals and NED error are the highest in C I5, we identify this as the region that likely experienced a significant spoofing event. We determined from an outlier at the beginning of the C I5 region (see Figure 5) that even the Galileo constellation is not immune to the spoofing in this scenario.
To further check the accuracy of our determination that GPS was spoofed, we evaluated the histograms of the Galileo error. With the biggest outlier in C I5 removed, we saw that the error appears relatively Gaussian, with some outliers and possible multi-modal behavior that were also seen in the nominal locations. The variance was higher than was observed at nominal locations, which could be attributed both to the presence of known RFI events, the fact that the nominal noise environment at the RFI event test has not been characterized (that is, it is possible there is a noisier nominal environment at this location), and that the “truth” position was not a known truth but obtained through post-processing of a dataset with increased RFI. Normalized error indicates that the error does not cross the 3σ threshold in any NED direction, further supporting the assertion that 3σ is a conservative threshold.
Important to note is that the major outlier around T+3.5 hours is visible in the NED plot (Figure 5), but the corresponding histograms do not contain that outlier. This indicates that the covariance also increases at that point. It dictates a need to monitor the covariance bound itself, as well as the positioning error. The NED time history plot and the raw error histograms serve this purpose, since it is clear that if we were to be only looking at the error normalized by 3σ, we would not have found significant evidence of the outlier, since the normalized error barely passes the 3σ threshold. This further supports our methods of combining multiple metrics, thresholds and visualizations rather than relying on a single metric to identify jamming and spoofing.
From the Galileo solution analysis, we increase our confidence that we have identified the regions with interference. We removed those areas and looked at the GPS vs. Galileo inter-constellation consistency difference. The normalized differences were now mostly within the 3σ threshold, and the raw error displayed some Gaussian behavior and is no longer on the order of the 105-meter error we were seeing in Figure 5. While these regions still have a higher error than nominal conditions and thus still display signs of interference, we are able to use our spoofing analysis to identify epochs in which we should not trust the GNSS. Using times outside those regions, we are able to figure out a reasonable truth position within 20 meters rather than 200 kilometers.
Positioning analysis using the inter-constellation consistency check is a powerful tool for determining the reliability of a position solution, even when the truth location is unknown. With the power metrics, we can further corroborate the positioning results, as well as find events indicating interference that the positioning metrics were unable to track.
FIGURE 6a. GPS pseudo range residuals for position solutions computed using only the GPS constellation. (Image: Authors)
FIGURE 6b. Galileo pseudorange residuals for position solutions computed using only the Galileo constellation for the DHS-sanctioned RFI testing event. (Image: Authors)
Next Steps and Summary
Leveraging the raw data collected by u-blox receivers in multiple locations with different nominal noise environments, we have developed the toolsets to do inter- and intra-constellation consistency checks to monitor for jamming and spoofing. Many further observables usable for RFI detection are being recorded by the u-blox receivers. Several power monitoring metrics have been evaluated in a preliminary analysis. The next step is to further characterize metrics such as C/N0, AGC and u-blox internal jamming metrics under nominal conditions.
In summary, the tools we have developed so far show that the u-blox receiver will allow for many different consistency checks on a variety of parameters to be running simultaneously. It would be difficult for a spoofer to interfere with all the dimensions we have covered in our detector. Continuously monitoring a wide variety of parameters will increase the chance that we are able to detect interference, thus lowering the chance that a spoofer is able to evade detection.
Acknowledgments
We gratefully acknowledge the support of both the FAA Satellite Navigation Team and The Aerospace Corporation under their university partnership program. We especially wish to thank Steve Lewis of Aerospace for his support and guidance throughout the development of this project. This article is based on the paper “Low Cost RFI Monitor for Continuous Observation and Characterization of Localized Interference Sources” presented at ION ITM 2022, the 2022 International Technical Meeting of the Institute of Navigation, Jan. 25–27, 2022.
LEILA TALEGHANI recently graduated with her MS degree from Stanford University in aeronautics and astronautics and is now a navigation engineer at Trimble.
FABIAN ROTHMAIER is a navigation research and development engineer at Airbus Defence and Space in Munich, Germany, and a former a Ph.D. student at the Stanford GPS Laboratory.
YU-HSUAN CHEN is a research associate at the Stanford GPS Laboratory.
SHERMAN LO is a senior research engineer at the Stanford GPS Laboratory.
TODD WALTER is a research professor in the Department of Aeronautics and Astronautics at Stanford University.
DENNIS AKOS is a professor with the Aerospace Engineering Sciences Department at the University of Colorado, Boulder.
BENON GRANITE GATTIS is a laboratory assistant and undergraduate student in the Aerospace Engineering Sciences Department at the University of Colorado, Boulder.
The European GNSS Agency (GSA) has issued a request for information (RFI) in preparation for the procurement of EGNOS geostationary navigation payload services.
The EGNOS space segment is provided by commercial satellite operators on the basis of service contracts. The GEO-1, GEO-2 and GEO-3 service contracts now cover the EGNOS space segment needs, and the GEO-1 and GEO-2 services will be the first of these to end, GSA reported. The GEO-1 and GEO-2 services will be replaced by new contracts, GEO-4 and GEO-5.
GSA is planning how it will replace the services delivered by the GEO-1 and GEO-2 satellites, and it’s issuing the RFI to collect information about opportunities to embark navigation payloads on board GEO satellites launched in a suitable time frame.
According to GSA, the results of the RFI will also be used to determine the best approach for the procurement of the payload services, which may be either procured at the same time or separately. It will help GSA define the tender specifications and decide on the most appropriate time to launch invitations to tender.
In addition, GSA aims to obtain information from owners of geostationary satellites that will be available for operational service from 2021 to 2027 and able to embark a navigation payload. The agency is specifically seeking information on future satellite plans and the possibility to embark SBAS payloads in due time to ensure an operational start date from 2021 to 2027.
The RFI will also request information service availability and long-term payload reliability; the process for EGNOS payload procurement, in-orbit testing and commissioning; information on the locations of the potential hosting sites for the EGNOS radio frequency uplink stations; and, finally, information on contractual arrangements, the payment scheme, and cost estimates, GSA added.
Answers to the RFI should be sent electronically to [email protected] by Aug. 31.
The RFI is an opportunity for industry and governments to submit ideas to define the issues so that global solutions can be proposed, debated and agreed on.
As UAS operations become more complex and are increasingly used for both commercial and recreational purposes, UAS traffic management systems, or UTM, are necessary to seamlessly integrate UAS into the airspace and existing air traffic management systems.
An operational UTM will ensure the safe and efficient use of the airspace as UAS operations become more complex, such as with established navigation routes and point-to-point route segments requiring specific equipage requirements. UTM will integrate UAS into the existing airspace infrastructure to ensure the continued safety of the airspace.
Any framework for a UTM will include many components, three of which are fundamental and will therefore be addressed as a matter of priority:
Registration system from which data is accessible in real time to allow remote identification and tracking of each UA, its operator/owner and location of the remote pilot/control station. To accommodate UA that are increasingly transported from one state to another for either recreational or professional use, this database should allow global access.
Communications systems for control of the UA and for tracking all UA within the UTM area. The communications system used for tracking UA must be able to identify when a manned aircraft is entering UTM airspace and provide an acceptable level of protection between it and UA operating in the airspace. Furthermore, it must facilitate detection of potential collisions with other UA and with obstacles such that appropriate avoidance action can be taken.
Geofencing-like systems that will support automatic updates by national authorities on the 28-day aeronautical information regulation and control (AIRAC) cycle to prevent UA operation in sensitive security areas and restricted or danger areas such as near aerodromes.
ICAO is soliciting proposals for a global framework for UTM ahead of its Drone Enable UAS Industry Symposium, which will take place in Montreal, Canada, in September.
“ICAO is the natural agency to be gathering together the best and brightest from governments and industry to define the problem so that global solutions can be proposed, debated and agreed on,” said Leslie Cary, ICAO remotely piloted aircraft systems program manager.
“Collaboration between stakeholders is key to addressing complex issues such as UTM,” added Brian Wynne, president and CEO of AUVSI. “AUVSI is pleased ICAO is taking steps to explore solutions for UTM that will allow companies to operate globally under the same standards, reducing barriers to innovation and improving safety and security for all aircraft – both manned and unmanned. We look forward to working with ICAO to draw awareness and facilitate industry engagement in the RFI process.”
For more information about the RFI, visit ICAO’s RFI website. Submissions need to be received no later than July 15.
The U.S. Department of Transportation (DOT) is studying responses to its November 2016 request for information concerning back-up systems for GPS. DoT is investigating possibilities and practicalities of using one or more positioning, navigation and timing (PNT) technologies to ensure PNT resiliency for critical infrastructure in the event of a temporary disruption in GPS availability.
The filing period closed Jan. 30.
RFI Response
Several companies responded to the RFI. Statements from Satelles, NextNav, NovAtel, Allied Partners, Harris, UrsaNav, and Orolia dba Spectracom were not made public because they “contain confidential business information data.”
Statements are available at the web page from Oakridge National Laboratory, UrsaNav and iPosi, SAE International, the GPS Innovation Alliance and Locata Corporation, which made its response openly available “to kick off the necessary public discussion.”
Senate Inquiry
At a Feb. 8 Commerce Committee hearing, Sen. Roy Blunt asked DoT Inspector General Calvin Scovel about progress on GPS back-up, which DoT and the Deputy Secretary of Defense announced they would “be working on” in 2015. Scovel responded with information about the Federal Aviation Administration’s next-gen plan, which did not address the question.
Sen. Blunt then asked Scovel to submit a written answer for entry into the final record of the hearing: “My question for the record will be that this commitment made in 2015 concerned about the current dependency that so many people have with GPS, is ‘Are they moving forward with a backup system if the current GPS system goes down?”
A controlled radiation-pattern antenna can preserve GNSS positioning while providing at least an azimuth angle towards an interference source. If integrated with an attitude and heading reference system (AHRS), only a few lines of position pointing towards the RFI source could provide a fast indication of the probable ground location.
By Gerhard E. Berz, Pascal Barret, Brent Disselkoen, Michael Richard, Vincent Rocchia, Florence Jacolot, Todd Bigham and Okko F. Bleeker
GNSS is an essential enabler for many aviation applications that rely on either accurate position or time synchronization. While the idea of “sole means” GNSS is disappearing, it remains challenging to match the performance and coverage of GNSS with terrestrial systems. This is why aviation is working on Alternate Positioning, Navigation and Time (A-PNT) to cope with the potential for a wide-area GNSS outage. Current navigation aids are clearly part of this approach in the short term. We will continue to need a terrestrial capability for some time, but we don’t expect that it will support the same level of performance as GNSS. Even if we have back-up, we must be able to resolve GNSS outages efficiently.
Among principal GNSS vulnerabilities — constellation performance issues, space/solar weather and radio-frequency interference (RFI) — RFI is the one where observability on the ground is often limited. While the protection of radio services from interference is a state responsibility typically assigned to a telecommunications or other government agency, it is in the interest of an air navigation service provider (ANSP) to be able to request help and enforcement action from the telecommunications regulator in an efficient manner.
As a part of its contribution to Single European Sky ATM Research (SESAR, a collaborative project to improve European airspace and its air traffic management), Eurocontrol has developed an RFI Mitigation Plan as a guidance framework with the objective to maintain risks to GNSS and the associated operations at tolerable levels. The document will be published by ICAO in its GNSS Manual in the new 2017 edition.
MITIGATION PLAN
RFI can be a security issue. Consequently, a commonly used philosophy in the security domain was used in the mitigation plan: there are many potential threats, but not necessarily all of them translate into operationally relevant risks. Threats are thus sort of dormant risks, which, if left to develop unmitigated, could develop into risks to aviation. The mitigation process monitors threats, assesses risks, and then implements suitable mitigation to stop threats from developing into risks. Three successive stages have been identified where such barriers can be applied:
Prevent transmission of RFI, mostly through radio regulatory actions and coordination;
Prevent interruption of positioning and navigation capabilities in the presence of RFI. This is achieved at the avionics level by making sure receivers can tolerate some RFI as well as redundant capabilities;
If interruption cannot be avoided, ensure that other communication, navigation and surveillance capabilities provide continued safety while being able to detect, locate and eliminate an RFI source efficiently.
This third barrier is where flight inspection or other aerial work platforms can play a significant role. However, this role is not limited to risk mitigation. Aerial measurement capabilities can also play a role in threat monitoring by getting data on RFI emissions that are too weak to pose operational risks, and facilitate risk assessment by providing a reliable reference of the impact of such signals on an aircraft in flight.
FLIGHT INSPECTION
Similar to the subject of flight validation, airborne GNSS signal-in-space testing must not necessarily rely on traditional flight inspection capabilities. Other aerial work capabilities can be used, and it is hoped that, over time, data from regular aircraft operations and event recording systems can be used at least for threat-monitoring purposes. However, as soon as a significant RFI occurs, purpose-built aerial detection and localization capabilities are hard to beat. Given that aviation is carrying the risks related to RFI, and telecom regulators are unlikely to have such capabilities, this naturally points to the experience and resources of flight inspection aircraft and their crews.
Even if a significant amount of ground-based RFI sensors are available, local building shadowing can make it difficult to impossible to detect and locate an RFI emitter. Aircraft-provided data can be superior to ground data, and a rough aircraft-based localization can greatly increase efficiency of ground-based localization and source elimination efforts. Aerial RFI localization capabilities offer unique strengths in an overall cooperative process.
EVOLVING SIGNALS
GNSS manifests the transition from analog signals of conventional navigation aids to digital ones. A common characteristic of digital signals is their better use of a frequency channel by spreading the carrier energy such that distinct carrier or subcarrier tones become difficult to observe. Unfortunately, RFI sources have kept up with this, and now most commonly employ swept CW signals, easy to produce but still looking essentially like broadband signals. Many unintentional RFI sources also look like broadband.
Because GNSS is a multi-modal system not uniquely used by aviation, a new type of RFI threat is becoming more common: intentional RFI, which is not directed at aviation, but may nonetheless have an impact. Because there is no direct intent to harm aviation, the nature of these signals and RFI scenarios can become diverse and unpredictable. Furthermore, given the prevalent and ubiquitous nature of GNSS, the number of potential RFI threats is more significant and will evolve more dynamically than aviation capabilities.
A recent effort collecting GPS outage data reported by pilots revealed that a small but surprising number of outages that could potentially be linked to RFI occur on a regular basis, even during en-route operations in some limited regions of the world. For flight inspection, this implies it would be useful to increase the sensitivity of RFI source detection commensurate with the digital nature of GNSS and consistent with the power levels that can impact receivers.
Another particular challenge comes from the specification of an interference mask for GNSS. Other navigation systems do not have such a mask, or any kind of minimum signal-to-noise ratio standard. The mask represents a realistically achievable interference environment. It has been adopted as a global benchmark where receivers experiencing signals above the mask may not produce misleading information, but may stop operating.
However, in practice, little is known about by how much typical receivers exceed the minimum masks. Some tests have reported a margin as significant as 23 dB to CW and 10 dB to broadband signals. This means that an RFI which may not bother one type of receiver at all could be a significant problem for another, limiting the possibility to rely on observed receiver performance. It also implies that signal-in-space effects should be detectable at the low levels of the ICAO receiver RFI mask.
CRPA LOCALIZATION
For civil aviation as opposed to military operations, a CRPA could make sense provided that it outperforms current RFI localization methods at a reasonable price. In military applications, the exact location of the RFI source may be of a secondary nature, as long as desired signal tracking can be maintained.
However, by steering a null (negative gain) towards the angle of arrival of an undesired signal source, a line or sector of possible source positions can be obtained. In this case, the main objective would not be to null a deliberate interferer or jammer, but to obtain a bearing on the type of the interferer. The main scenario we worry about that leads to low-power events are those where aviation is not the desired target, such as a PPD. Unintentional cases can be a mix of high- or low-power cases. The use of a GNSS-specific antenna is expected to provide the required sensitivity, while being able to profit from the military off-the-shelf development. When further integrated with standard flight-inspection sensors such as an attitude and heading reference system (AHRS) and additional geolocation software, this approach has the potential to increase the reliability, accuracy and speed of geolocation while reducing operator effort and flying time. An additional potential benefit is the preservation of ownship position when flying into an area of significant RFI.
The suggested use of military technology brings with it the question on how such use could be authorized. CRPA antennas and associated antenna electronics manufactured in the United States fall under the International Traffic in Arms Regulation (ITAR). While this is a solvable but, nonetheless, cumbersome issue, the approach taken by this project was first to evaluate possible benefits from using a CRPA before worrying about the ITAR issue.
This study was conducted by Eurocontrol in the frame of a SESAR Project on GNSS, including a contract with Rockwell Collins for a feasibility study of the CRPA RFI localization concept. The French (DSNA/DTI) and U.S. FAA Flight Inspection service supported the project with expertise and in-kind contributions. The FAA conducted an overflight with a direction-finding-equipped aircraft for direct comparison between the CRPA approach and other, non-GNSS specific, commercial solutions.
TECHNOLOGY OPTIONS
Current, common GNSS CRPAs come in either 4- or 7-element variants. CRPAs always require antenna electronics for further processing of the RF inputs, and perform either nulling (steering negative gain towards RFI sources) or beamforming (steering positive gain towards GNSS satellites), or both. The most performant system is a 7-element CRPA in combination with digital beam-former antenna electronics. The 7-element CRPA has a diameter of 36 cm (14 inches), which is of some concern for installation on a typical flight-inspection aircraft such as the Beech King Air. But for a feasibility study, it makes sense to first evaluate the most-performing option. If there is unnecessary margin, the solution can be simplified afterwards.
A top-mounted solution on the airplane fuselage was retained due to experience with military anti-jam performance suggesting that RFI localization performance would be sufficient while retaining the benefit of stable ownship position. A key element of the assessment focused on how to best use aircraft banking to facilitate geo-localization.
As shown in Figure 1, the CRPA is connected to the Digital Integrated GPS Anti-Jam Receiver (DIGAR). As there is one RF cable per CRPA element, it is useful to install the DIGAR as close as possible to the CRPA. The standard military-production DIGAR contains not only the antenna electronics but also the receiver including baseband processing. For civil purposes, either a civil receiver would need to be integrated into the DIGAR or, alternatively, a single RF output is available to connect a standard civil GPS receiver. The DIGAR will also feed angle-of-arrival information into a direction-finder software.
Figure 1. System configuration. Source By Gerhard E. Berz, Pascal Barret, Brent Disselkoen, Michael Richard, Vincent Rocchia, Florence Jacolot, Todd Bigham and Okko F. Bleeker
The software provides angle-of-arrival information with respect to the antenna/aircraft reference frame. To provide a geolocation capability, this must be combined with ownship position and aircraft attitude. As most flight inspection aircraft are equipped with an AHRS, this is not expected to be a problem. Project resources did not permit full integration, so testing was done using the direction-finder display only. The AHRS would need to provide 10–50 Hz updates with an error of not more than ±2 degrees.
Figure 2 shows an example of the direction-finder output. Lighter areas show where the antenna electronics produce negative gain, while darker areas represent stronger positive gain. The red dot indicates a potential interferer has been identified. Source location is at about 280 degrees of azimuth with respect to aircraft nose.
Figure 2. Excerpt from direction finder polar display of RFI signal angle of arrival. Source By Gerhard E. Berz, Pascal Barret, Brent Disselkoen, Michael Richard, Vincent Rocchia, Florence Jacolot, Todd Bigham and Okko F. Bleeker
Correct detection probability will depend on the sensitivity threshold and associated false detection probability being considered acceptable. A visual localization may still be possible at carrier-to-noise density ratios (C/N0) below those needed to produce the red dot here, especially if the visible ambiguity can be removed through some aircraft maneuvering. It can be inferred from the system description that once the full integration is accomplished, the provision of a direct output using only a few lines of position to find a probable RFI source location in terms of approximate lat/long coordinates should be straightforward.
SIMULATOR TESTING
A well-calibrated simulator capable of feeding the seven RF inputs was used to assess detection performance for different flight patterns near an RFI source. The tested patterns include a rectangular, a circular and an oscillating, S-shaped trigger-and-hunt trajectory. A variety of different encounter scenarios in terms of power levels and free space path loss were tested. Power levels were adjusted to produce a 1-dB reduction in the C/N0. Both a continuous wave (CW) interferer at the L1 center frequency and a broadband (BB) interferer were simulated (using a 20-MHz-wide PSK signal). Figure 3 shows an example of achieved detection accuracies in both azimuth and elevation angle.
Figure 3. Example result of angular detection performance. Source By Gerhard E. Berz, Pascal Barret, Brent Disselkoen, Michael Richard, Vincent Rocchia, Florence Jacolot, Todd Bigham and Okko F. Bleeker
While there is a strong peak within ±10 degrees of azimuth, there are also significant outliers. For the elevation (note the normalized scale), however, the main peak is thinner with even stronger sidelobes. Due to the installation of the antenna on top of the aircraft fuselage, the simulation results indicate that the elevation angle output is not very useful for detection. The time series result for the azimuth is given in Figure 4, where it can be seen that there are many good detection matches but also some “sympathetic nulls” that move in the opposite direction of the ground track truth reference (circled in grey). It is expected that with additional software processing, these sympathetic nulls can be filtered out.
Figure 4. Azimuth Time Series Result Corresponding to Figure 3. Source By Gerhard E. Berz, Pascal Barret, Brent Disselkoen, Michael Richard, Vincent Rocchia, Florence Jacolot, Todd Bigham and Okko F. Bleeker
For all tested scenarios (assuming additional filtering), azimuth detection capability was better than ±10 degrees (one standard deviation), and in some cases as accurate as ±2 degrees. There was no significant difference between CW and BB results. As could be expected, simulated aircraft banking significantly improved detection capability. Consequently, the use of orbits seems to be the best search strategy. The simulator testing used a figure-eight pattern with one of the orbits passing over the interference source.
LIVE-SKY VAN TESTING
Rockwell Collins has an authorization to broadcast RFI test signals at the GNSS L2 frequency. Previous work showed that the results at L2 can be applied equally to L1. Figure 5 shows the test area, including a –100-dBm signal level boundary. The interferer was installed on a tripod and fed by a signal generator using a normal GPS fixed radiation pattern antenna (FRPA).
Figure 5. Live-sky test area. Source By Gerhard E. Berz, Pascal Barret, Brent Disselkoen, Michael Richard, Vincent Rocchia, Florence Jacolot, Todd Bigham and Okko F. Bleeker
Locations B and C were used to both calibrate the RFI level and as check points for the van trajectory. The test van included a fixture that allowed a tilting of the CRPA by 30 degrees from zenith to either side. Figure 6 shows a schematic of the tilt fixture. It can be seen that this set up creates a realistic RFI path that arrives with an elevation slightly below the horizon at the unit under test. Two sets of tests were performed: one where the van drove straight into or out of the area of interference to determine overall equipment sensitivity, and varied paths to quantify angular detection performance. Again, both CW and BB RFI signals were evaluated.
Figure 6. CRPA with tilt fixture. Source By Gerhard E. Berz, Pascal Barret, Brent Disselkoen, Michael Richard, Vincent Rocchia, Florence Jacolot, Todd Bigham and Okko F. Bleeker
Not surprisingly, elevation angle results turned out not to be very reliable given the below horizon signal path. But azimuth errors were slightly greater than obtained during the wavefront simulator testing (±12 degrees, one sigma). This can be attributed to both multipath and a less accurate heading truth reference. Taking these additional factors into account, the results are very consistent. Tilting the antenna by 30 degrees towards the RFI source significantly improves azimuth resolution (to about ±8 degrees) while also reducing sympathetic nulls. When the tilted antenna points away from the RFI source path, azimuth accuracy will decrease, which is considered helpful in avoiding false detections.
Summary. Even if a good bit of integration work remains necessary to produce a production-ready system for flight inspection or other similar aircraft, the approach shows promise. Further testing, especially using an actual aircraft installation, is recommended. Installation of a 7-element CRPA will be challenging on a typical Beech King Air, but possible. Antenna calibration requirements are expected to be manageable with a standard network analyzer. To avoid further complications with export regulations, the use of a separate civil GNSS receiver is recommended. The overall system is, at this stage, still on the costly side.
While a 4-element CRPA could be used, this was estimated to double or triple angular azimuth detection errors and reduce the detection distance, and consequently not likely to be worth the additional cost. While smaller 7-element CRPAs than the one used are available, their performance would need to be assessed.
For a top-mounted CRPA, aircraft banking is essential to ensure good performance. This could increase the amount of airspace required for detection and lead to operational complications. Furthermore, since the aim is to increase detection sensitivity to geo-locate weak power sources such as personal privacy devices, maintaining ownship position is not that critical, as it can be managed by maintaining an appropriate distance from the RFI source if needed. Consequently, both DSNA and FAA recommend using a bottom-mounted CRPA. In addition to adding 10 dB of detection sensitivity on average and reducing the need for maneuvering, it may restore the utility of the elevation output, thereby potentially further reducing search time. Either way, it will be useful for equipped aircraft to have alternate positioning capabilities to GNSS both for aircraft guidance and truth reference systems.
The system required a 15-dB stronger signal to transition from detection to localization. However, this is dependent on the accepted false-alarm rate. A tunable procedure can be envisaged where the software accepts a higher false-alarm rate at first to maximize search capability and moving to a lower alarm rate to confirm suspected RFI source locations later. Both the potential of the additional filtering software and any human-machine interface aspects would need to be further evaluated.
GENERIC CAPABILITIES
The two common options for in-flight detection of RFI sources in any relevant frequency band are the use of either a spectrum analyzer or, if available, a direction finder. The spectrum analyzer approach depends on connection to a suitable antenna, preferably with some directionality. In this way, the aircraft can be maneuvered to point the antenna either towards or away from the RFI source. Normally there is very little directivity, making this a challenging search. A direction finder is a significant improvement. Figure 7 shows the L-band antenna array used by a DF-4400 as installed on the bottom of the aircraft.
Figure 7. CRPA with tilt fixture. Source By Gerhard E. Berz, Pascal Barret, Brent Disselkoen, Michael Richard, Vincent Rocchia, Florence Jacolot, Todd Bigham and Okko F. Bleeker
Newer generation spectrum analyzers with a good GNSS-specific pre-amplifier, using digital sampling with a fast A/D converter, could provide useful capability. However, the subject is beyond the scope of this discussion, and we focus here on comparing the CRPA approach with a standard direction finder.
The FAA Flight Inspection service conducted complementary flights during the Rockwell Collins live-sky van testing. The flights included orbits and a direct overflight of the RFI source. This was complemented by additional laboratory calibration to ensure that results could be compared. The sensitivity results of the CRPA approach are more meaningful in comparison with a generic direction-finder capability. Since test data is only available for a top-mounted CRPA, the comparisons here are made for the preferred bottom-mounted CRPA using engineering estimation.
The key finding was that while direction-finding capability was quite comparable between the CRPA- system and the DF-4400 for CW, the CRPA-system outperforms the DF-4400 by a significant margin when encountering broadband signals. This is considered to be a significant improvement given the expected nature of RFI sources. During the FAA overflight, the aircraft did not manage to detect the broadband signal. Consequently, the values given here are reconstructed from laboratory analysis. Table 1 compares the estimated achievable sensitivities.
Table 1. Comparison of direction-finding sensitivity. Source By Gerhard E. Berz, Pascal Barret, Brent Disselkoen, Michael Richard, Vincent Rocchia, Florence Jacolot, Todd Bigham and Okko F. Bleeker
In view of the limitations of the data analysis performed, these values must be interpreted with caution. In general, we can conclude that the direction-finding sensitivity of the CRPA system is relatively insensitive to the encountered modulation of the RFI signal, and that the bottom-mounted CRPA system outperforms the DF-4400 system by a small margin in the CW case and by a large margin in the broadband case. How many additional dBs can be gained by both approaches through further optimizations is for future analysis. The performance improvement of the CRPA system does come at a cost, as could be expected.
DETECTION
Before the search for an RFI source can begin, it must be detected. Normally it should be easier to detect an RFI source than to locate it, since direction-finding requires a certain signal strength to obtain bearing information. However, given the directionality of DF arrays, this may not necessarily be true. Another potential factor is the reliance on a spectrum analyzer to detect RFI, which may not achieve the corresponding noise floor, especially when using a broad scan across a wide frequency range. The direction-finder system needs about a 15-dB difference between detection and localization ability.
Figure 8 shows the detection ranges for the top-mounted CRPA system for a given ground-based emitter while the aircraft altitude is assumed at 2000-ft AGL. The bottom mounted system would improve the minimum detection threshold further. Given that 15 dB can translate into a significant difference in free space path loss distance, concepts for efficient direction finding once an RFI source is detected deserve further attention.
Figure 8. Detection ranges for top-mounted CRPA system. Source By Gerhard E. Berz, Pascal Barret, Brent Disselkoen, Michael Richard, Vincent Rocchia, Florence Jacolot, Todd Bigham and Okko F. Bleeker
HUMAN FACTORS
During the FAA overflight, the broadband RFI couldn’t be detected by either the spectrum analyzer in use or the DF-4400. Part of the challenge was using the right equipment settings. For the DF-4400, it was found that best performance could be obtained for detecting broadband RFI when using the FM wide mode of demodulation. Similar findings were obtained for the use of the spectrum analyzer, where specific skills are necessary to use the equipment to its fullest capability. Similar issues are expected when having to interpret the display of a CRPA-based system. This means that regardless of the RFI source geo-location approach used, specific training should ensure that aircraft operators have the greatest chance of success in finding RFI sources.
CONCLUSIONS
An approach using a CRPA antenna, electronics and processing software proved superior to current, generic direction-finding capabilities, especially with respect to broadband signals. Maintaining ownship position in the presence of RFI is a secondary objective when looking for the expected weak signal sources, and the use of a bottom-mounted CRPA system is preferred. Additional filtering to eliminate sympathetic nulls and other issues require further investigation.
Significant benefit derives from employing aerial work aircraft in cooperation with ground-based capabilities. We recommend that equipment manufacturers further study all aspects of GNSS RFI geo-location and improve their capabilities. Such capabilities are expected to limit the exposure time to RFI cases and allow a more efficient deployment of ground-based spectrum enforcement resources. These studies should include the improvement of detection and localization equipment, and the development of corresponding operational procedures for flight crews.
ACKNOWLEDGMENTS
The Eurocontrol-funded contract with Rockwell Collins is part of the Eurocontrol contribution to SESAR Project 15.3.4, GNSS Baseline and the GNSS RFI Vulnerability Mitigation Task.
Rockwell Collins provided the DIGAR and Direction Finder Software.
This article is based on a paper presented at ION-GNSS+ 2016.
Disclaimer. This article does not contain any official Eurocontrol, SESAR, FAA or DSNA position or policy. It does not constitute any endorsement of a particular product, or a statement of any kind relating to any future procurement activity.
GERHARD BERZ and PASCAL BARRET work at Eurocontrol, Belgium; VINCENT ROCCHIA and FLORENCE JACOLOT with Direction des Services de la Navigation Aerienne, France; BRENT DISSELKOEN and MICHAEL RICHARD at Rockwell Colins, U.S; Okko F. Bleeker with OFBConsult System Engineering, the Netherlands; and TODD BINGHAM with the U.S. Federal Aviation Administration.
The White House Office of Science and Technology Policy (OSTP) is seeking public input on development of the second U.S. National Plan for Civil Earth Observations, the 2017 National Plan for Civil Earth Observations.
Today, the Federal Register posted OSTP’s Request for Information (RFI) on the development of the plan, which will build upon the priorities and supporting actions identified in the 2014 National Plan for Civil Earth Observations. Through it, OSTP aims to advance the United State’s capabilities to ensure stable, continuous and coordinated Earth observations for the benefit of society.
The public input provided will inform OSTP as it works with federal agencies and other stakeholders to develop the plan.
OSTP welcomes input to develop the plan, and encourages anyone interested to respond via the RFI’s electronic template (to be posted here), which should be sent to [email protected].
Comments of up to approximately 2,000 characters per question are requested and must be received by 11:59 p.m. (Eastern Time), July 15, 2016, to be considered.
When subjected to very strong interference, a GNSS receiver can be totally blinded and stop working. This is often the scope of intentional jammers. However, in a number of cases the presence of interference is severe enough to significantly decrease receiver performance, but not so much as to make the receiver lose its lock on the satellite signals or blind the acquisition of the satellite signals.
Such intermediate power values turn out to be the most dangerous cases, because sometimes they cannot be detected, but lead to a worsening of the positioning performance. The accuracy of the position solution depends on, among others, the quality of the pseudorange measurements and/or the phase measurements. Thus, when radio-frequency interference (RFI) degrades the pseudorange and phase measurements or induces cycle slips on the phase measurements, the accuracy of the position solution will decrease.
Impact on the Front End
The front-end filters the incoming signal, demodulating it to the chosen intermediate frequency before performing the analog-to-digital conversion (ADC).We must consider the presence in the front end of the adjustable gain control (AGC) between the analog portion of the front end and the ADC. When the GNSS band is interference-free, AGC gain depends almost exclusively on thermal noise, since the received signal power is below that of the thermal noise floor. When in-band interference is present, the AGC will squeeze the incoming signal to match the maximum dynamics of the ADC, causing a reduction of the amplitude of the useful signal, which may be lost. This may typically happen in the presence of some kind of wide-band interference (WBI) spread over a bandwidth larger than the passband of the front-end filter.
With narrow-band (NBI) or continuous-wave interference (CWI), statistics of the digital signal at the ADC output are also affected. In this case the AGC can still compress the input signal to avoid a stronger saturation, but the following receiver stages will have to deal with a GNSS contribution quantized only on lower levels.
In the presence of stronger interference, even the other components of the front end (filters and amplifiers) may be led to work outside of their nominal regions, generating nonlinear effects or clipping phenomena (in which the signal amplitude exceeds the hardware’s capability to treat them). In both cases, spurious harmonics are generated and mixed with the useful signal in the front end itself.
Impact on the Acquisition Stage
If the interference is not driving the AGC/ADC to full saturation, the acquisition module is still able to perform its task, processing the interfered signal to estimate the code phase and the Doppler shift with respect to the local code. The correlation with the local code can be seen as a spreading operation followed by a filter.
Figure 1. GPS L1 C/A acquisition search space in (a) an interference-free environment and in the presence of (b) –140 dBW in-band CWI; (c) –135 DBW in-band CWI; (d) –130 dBW in-band DWI.
Figure 1 showsthe acquisition search space for different levels of theinterfering power of a CWI from –140 to –130 dBW compared to the interference-free case. The search spaces depicted for the four scenarios are achieved using 1 ms of coherent integration time and three non-coherent accumulations, and the peak-to-noise-floor separation defined as
is considered as a figure of merit. The value of αmean decreases as the interfering power increases, thus increasing the probability of a false alarm. With the increasing power of the CWI, a modulation effect in the search space floor in the Doppler domain dimension can be observed. Such an effect is mainly determined by the new harmonics components generated by the multiplication between the locally generated carrier and received CWI. Such an effect also depends on how the interfering signal and the useful GNSS signal are combined at the entrance to the acquisition block, which in turn depends on the random variables φ0 and θint.
In the presence of WBI, a different effect is observed in the acquisition search space. Considering a band-limited Gaussian white noise spread all over the GNSS useful filtered signal components, the effect on the CAF envelop is an increase in the noise floor. This increases the search space noise floor. The presence of additive band-limited noise causes a uniform increase in the noise floor tin the search space that might mask the correct correlation peak and thus fool the acquisition process.
Impact on the Tracking Stage
Interference impact on the tracking stage has a direct consequence on the quality of the measured pseudorange. Harmful interfering signals increase the variance of the time-of-arrival (TOA) estimate by the discriminator and modify the shape of the S-curve of the code discriminator, thus creating in some cases a bias in the measurements.
Figure 2 depicts outputs of the early-prompt-late correlators. In the presence of in-band CWI and of NBI, the interference is injected 9.3 seconds after the beginning of the tracking stage where the receiver is correctly locked on the received signal. A CWI, shifted 200 kHz with respect to the signal intermediate frequency (in correspondence with a C/A code spectrum line), increases the noise at the correlators outputs and leads to harmonic behavior of the early-prompt-late correlator outputs.
Figure 2. GPS L1 C/A code tracing error comparison: coherent and non-coherent early-late processing (CELP and NELP).
NBI increases the variance of the correlators’ outputs; this directly increases the pseudorange error and the noise on the receiver phase measurements. Additive band-limited noise leads to an overall increase in the carrier phase discriminator output variance over the 3σ threshold, which for a PLL two-quadrant arctangent discriminator is 45 degrees. When in presence of strong CWI, a sudden jump of the phase discriminator output is detected as soon as the CWI is injected onto the received signal.
Impact on the Estimated Signal-to-Noise Ratio
Sticking to the definition of C/N0 as the ratio between the received power and the power spectral density due to thermal noise at the input of the receiver, the presence of interference should not change the value, since the thermal noise is not increasing. However, the C/N0 value provided by the receivers is estimated on the basis of the correlator outputs at the tracking stage. For this reason the estimation is affected by the presence of the additional (nonthermal) noise generated by the interference. The variation of the C/N0 can also be used as observable for interference (or other threats) detection.
Chapters: The Interference Threat; Classification of Interfering Sources and Analysis of the Effects on GNSS Receivers; The Spoofing Menace; Analytical Assessment of Interference on GNSS Signals; Interference Detection Strategies; Classical Digital Signal Processing Countermeasures to Interference in GNSS; Interference Mitigation Based on Transformed Domain Techniques; Antispoofing Techniques for GNSS. The book is intended for members of the engineering/scientific community with pre-existing knowledge of satellite navigation principles and GNSS.
FabIo Dovis holds a Ph.D. in elecronics and communications engineering from Politecnico di Torino, Italy, where he is an associate professor.
The United States Agency for International Development (USAID) is seeking services from companies to expand its existing geospatial technologies. USAID’s mission is to support developing nations, and its GeoCenter geospatial tools help map and manage its global projects.
In a request for information issued on Jan. 20, USAID said: “The purpose of this RFI is to solicit input from organizations involved in managing, analyzing and visualizing data, particularly for the purposes of informing policy and decision-making in international development. In this RFI, we seek to gather information about the scope of a draft Statement of Work (SOW) and the community’s capabilities to fulfill these requests. In particular, we want to understand the available expertise, the feasibility, and challenges faced in responding to the services outlined in our draft SOW.”
USAID’s Data and Analytics team is seeking to partner with external organizations to provide support for data management, analysis, and visualization, particularly in the following five areas:
Data analysis and visualization
Research to contextualize development efforts and challenges
Data infrastructure and information sharing
Futures analysis and scenario planning
Training and support to build agency and host-country capacity in data and analytics.
