In 2019, military forces witnessed the global threat of GPS/GNSS interference grow, with more sophisticated threats and increasing military demand for assured operations in Navigation Warfare (NAVWAR) and GPS-denied environments.
Enemy forces are deploying more advanced jamming and spoofing technologies worldwide, jeopardizing the security and reliability of positioning, navigation and timing (PNT) data that feeds into GPS receivers, downstream networks and subsystems.
Military forces must vigilantly protect their information advantage from malicious attacks by delivering situational awareness, mission planning and warfighter solutions.
For these priorities, proven and efficient signal integrity solutions will be even more critical in 2020.
Requirements to Ensure Signal Integrity in 2020
Any critical system that relies on PNT data should go into the field with two known states:
First, it should withstand a GPS outage during testing and simulation — including rigorous jamming and spoofing simulation to predict how the system will react under various conditions. Simulation scenarios can vary in complexity, and newer software-defined simulators provide flexibility to meet current requirements while future-proofing investments in test equipment.
Second, the system should have a signal threat detection and alert mechanism. Critical systems also need backup layers such as anti-jam antennas, threat mitigation technology and alternative encrypted signals to ensure continuous operations, even in compromised environments.
Going into 2020, GNSS simulation and interference detection and mitigation (IDM) will continue to adapt to emerging threats and provide the essential foundation for Assured PNT.
For more about Resilient PNT and NAVWAR solutions, visit www.Orolia.com.
With the proliferation of jamming devices readily available, maritime vessels need to be situationally aware of GNSS interference and disruption. The threat of GNSS jamming is made even more critical in situations that require navigation through narrow straits under poor visibility, with no sea lane markers in sight.
Case in point being the British-flagged oil tanker Stena Impero, which was seized by Iran’s Revolutionary Guards while sailing through the Strait of Hormuz. Iranian Islamic Revolutionary Guard Corps stated that Stena Impero had taken a wrong route when entering the Strait of Hormuz.
This happened during a time when an advisory warning by the U.S Maritime Administration had already been released, stating that vessels operating in the Persian Gulf, Strait of Hormuz and Gulf of Oman may encounter GPS interference, bridge-to-bridge communications spoofing or other communications jamming with little to no warning.
This episode could have been avoided if better awareness of the navigation system was employed.
Jamming protection at sea. ST Engineering has developed AgilLOC Antenna Element Compact (AEC), which provides GNSS protection against three simultaneous jamming/interference sources with its adaptive nulling algorithm for the maritime sector, ensuring continuous GNSS protection to connected systems.
AgilLOC AEC was designed for easy integration with new or existing legacy systems that required uninterrupted GNSS reception. Despite its lightweight and compact design, AgilLOC AEC provides a robust response to narrow and wideband interference, the company said.
When disruption happens. In a disruption, the crew onboard can only rely on radar or cross bearings using compass, terrestrial radio navigation or even sextants.
The loss of GNSS input to the ship’s surface search radar, gyro units and electronic chart display and information system (ECDIS) will result in a lack of GNSS data for position fixing, radar over ground speed inputs, gyro speed input as well as the loss of collision avoidance capabilities on the ECDIS radar display. It is imperative that all ship’s crew are aware of the status of their GNSS reception.
Many GNSS receivers currently installed onboard vessels do not provide for jamming monitoring or mitigation. Deliberate or unintentional GNSS inference are becoming more prevalent, increasing the risk of receivers being overwhelmed by elevated levels of interferences.
Satellite navigation is essential for all maritime applications under all weather conditions. The AgilLOC AEC protects the GNSS signals for a smooth navigation and precision landing alongside with other navigational systems.
The Official Journal of the European Union (EU) will publish a funding opportunity in the near future for a GNSS “Advanced Interference Detection and Robustness Capabilities System,” according to officials familiar with the project.
Advance notice of this procurement was first given in August of last year, with an award projected for the first quarter of 2019. Some observers have speculated that the procurement delay was related to a change in how the final system is envisioned. The current version of the notice asks for a crowdsourcing, software and networked-based solution.
The advance notice calls for the vendor to both establish the system and operate it.
The purpose of the present tender is to establish a new mechanism to detect interference at receiver and antenna level based on crowdsourcing and sharing information coming from any user (individuals or associated ones) and run the service for a period of two years.
While for many “crowdsourcing” suggests the participation of large numbers of individuals, this will likely not be part of the scheme. Speaking to a government advisory board, Jean Yves Courtois, CEO at Orolia, said that battery drain on cell phones would prevent this from being practical. “Privacy concerns would also be an issue,” he said. Each individual would have to affirmatively agree to have their location information used continuously. This additional administrative burden would be significant.
Much easier and preferable would be using Information from already deployed networks of fixed receivers, such as base stations. Unchanging locations and existing network connections make the engineering easier and thorny privacy concerns would be minimized. These ideas are also reflected in the current version of the advance notice:
The activity shall also focus on identifying and engaging users (such as entities currently monitoring vast networks of devices integrating GNSS receivers) by means of an appropriate enrolment scheme ensuring the provision of the data. The design of the system shall ensure that the sensitivity of the data (GNSS vulnerabilities) is always protected.
Crowdsourcing and collating such information is seen by many in industry as a relatively straight-forward engineering problem. Representatives from Orolia and Microsemi, for example, included ideas about crowdsourcing disruption data in recent presentations to the U.S. PNT Advisory Board. Both agreed, though, that there are few commercial incentives to do such work without a government customer.
It is perhaps not a surprise that the EU is taking the lead in this field while other GNSS providers seem to have little interest.
Unlike GPS, GLONASS and BeiDou, which are first and foremost national security systems, Europe’s Galileo was built and is operated by a civil organization focusing on economic and civil benefits. Interference with signals directly undercuts these benefits and can be easily seen in direct economic costs.
Many European countries are using GNSS for road tolling, for example. Small GNSS jammers are easy to acquire off the internet and their illegal use is likely costing nations millions of euros in lost tolls each year. Without the ability to regularly detect, sanction, and deter this activity financial losses will continue to mount.
The interference with tolling problem is not specifically addressed in the EU’s advance notice. It may well be that tolling authorities and others will be expected to install their own application specific interference detectors and then encouraged to link them to the EU backbone and database.
The European Commission has been aware of this vulnerability for some time. In 2015 it contracted with Nottingham Scientific Ltd. in the UK to lead a multi-nation team and assess the extent of the problem.
The STRIKE3 project was in operation from February 2016 to January 2019. Its goals were to sample and classify interference events, recommend a standard event reporting scheme, and assess the vulnerability of different types of GNSS receivers.
The project’s sampling activity in 23 different countries detected nearly 500,000 interference events. Of these, 59,000 were classified as deliberate attempts to disrupt GNSS signals.
Within the deliberate events the STRIKE3 team were able to identify about 300 jammer “families,” according to Mark Dumville, Co-Founder and Director at Nottingham Scientific. Along with the jammers they were able to classify into groups, there were “some very interesting outliers,” Dumville said. “These are likely evidence of jammer technology continuing to develop and evolve.”
STRIKE3 is viewed as a very successful project by most everyone in the international PNT community, and certainly within the EU, according to officials.
The upcoming announcement and future establishment of an on-going interference detection capability are some of the next logical steps to better securing Europe’s PNT services.
New initiatives from the Navigation Innovation and Support Programme (NAVISP), a program of the European Space Agency (ESA), have targeted counter-jamming and counter-spoofing efforts, as Europe’s Galileo program gains progressive foothold in the marketplace, particularly in safety-critical systems such as driverless cars.
“We are looking for new and disruptive ideas in navigation and that is why we created NAVISP,” said ESA Director General Jan Wörner.
TeleConsult Austria is working with JH Joanneum University of Applied Sciences on the GNSS Interference Detection and Analysis System (GIDAS), to automatically detect, classify and pinpoint all intentional interference sources within a given area by monitoring all civil GNSS signals in real time.The aim is to build a multi-frequency scalable system. GIDAS plans to begin commercialization at the end of 2019.
France Developpement Conseil has developed a hardened satnav module called DRACONAV, combining hardware and software to combat jamming and spoofing. Targeting intelligent transport applications, it seeks to identify cyber attacks and continue to provide authenticated positioning information as they occur.
DRACONAV would deliver a level of confidence to let users know if they can continue relying on the data the module delivers, and yield an estimate of the receiver’s true position as the attack continues. A prototype design has undergone more than 3,000 kilometers of field tests and is moving to industrialization.
o to analyze a few tens or hundreds of milliseconds of Galileo signals at a time, to tell the user whether or not the signal is authentic or spoofed.
In Romania, InSpace Engineering’ MARGOT assesses the multipath and interference impact on PNT information in maritime environments.
The Norwegian company SINTEF is developing its Advanced Radio Frequency Interference Detection, Alerting and Analysis System (ARFIDAAS) project, offering as wide a spectral coverage as possible — including all current GPS, Galileo and GLONASS signals — to identify disruptions due to intentional or unintentional interference.
UK company Helix Technologies has developed compact helical antennas, built around a dielectric ceramic core, primarily for driverless cars. The multi-frequency design aims to reduce susceptibility to interference as well as multipath. Testing will soon get underway in several European cities.
Analysis of Satellite Data Exposes Threats to Civil Aviation
The Russian Federation is growing and actively nurturing a comparative advantage in the targeted use and development of GNSS spoofing capabilities to achieve tactical and strategic objectives at home and abroad.
Cover: C4ADS
A new report titled “Above Us Only Stars: Exposing GPS Spoofing in Russia and Syria,” presents findings from a year-long investigation ending in November 2018 on an emerging subset of electronic warfare (EW) activity: the ability to mimic, or spoof, legitimate GNSS signals to manipulate PNT data.
Using publicly available data and commercial technologies, the authors detect and analyze patterns of GNSS spoofing in the Russian Federation, Crimea and Syria. They profile different use cases of current Russian state activity to trace the activity back to basing locations and systems in use.
The report is issued by C4ADS, a Washington, D.C.-based nonprofit organization dedicated to providing data-driven analysis and evidence-based reporting on global conflict and transnational security issues. Its website, c4ads.org,lists transnational organized crime, proliferation networks (rogue nations and non-state actors), threat finance and supply-chain security as areas of focus.
Pinpointing interference. Todd Humphreys, a University of Texas at Austin associate professor and head of the university’s Radionavigation Laboratory, collaborated on the research underpinning the report.
Humphreys stated that, as far as he knew, the study constitutes the first characterization of GNSS interference from space, and cited “some interesting findings:
“Using Automatic Identification System (AIS) data captured by overhead satellites, we monitored spoofing in the Black Sea, around St. Petersburg, Archangelsk, etc., and built a picture of interference activity that spans two years. All such activities occur near Russian coastal waters.
“Correlating this activity with the travel schedule of the Russian head of state, we have strong evidence that the spoofing is a protective measure used to thwart drone attacks on Vladimir Putin.
“By exploiting a software-defined GNSS receiver my lab is operating on the International Space Station, we were able to pinpoint a powerful source of interference, which we found to be coming from the northwest quadrant of a Russian-operated airbase in Syria. This explains the many reports of GNSS interference in the eastern Mediterranean during the past year.”
Global Threat. The tools and methodologies for perpetrating GNSS interference are proliferating at a rapid rate, and the frequency of such incidents around the world increases steadily. GNSS attacks, and GPS attacks specifically, now constitute an active, present, disruptive strategic threat in every theater of operation.
The C4ADS website, in announcing the report, states that “The Russian Federation has a comparative advantage in the targeted use and development of GNSS spoofing capabilities. However, the low cost, commercial availability and ease of deployment of these technologies will empower not only states, but also insurgents, terrorists and criminals in a wide range of destabilizing state-sponsored and non-state illicit networks. GNSS spoofing activities endanger everything from global navigational safety to civilian finance, logistics and communication systems.”
Examining GNSS spoofing events across the entire Russian Federation, its occupied territories and overseas military facilities, the report identifies 9,883 suspected instances across 10 locations that affected 1,311 civilian vessel navigation systems since February 2016. It demonstrates that these activities are much larger in scope, more diverse in geography, and longer in duration than any public reporting suggests to date.
C4ADS believes the Russian Federal Protective Service (FSO) operates mobile systems to support this activity. It chronicles the use of GPS spoofing in active Russian combat zones, particularly Syria, for airspace-denial purposes. This capability is scarcely reported in the public domain. C4ADS identified ongoing activity that poses significant threats to civilian airline GPS systems in the region.
Centum Solutions of Madrid, Spain, introduced its NO JAM ZONE interference detector ION GNSS+ in Miami.
NO JAM ZONE is Centum’s solution for GPS interferences detection and location.
Centum will have a booth in the ION GNSS+ exhibit hall to showcase NO JAM ZONE, as well as and a GNSS simulator to show the process of detection and location of different sources of interference.
Critical infrastructures need to ensure the proper functioning of their radio frequency systems. A good example of this is airports. NO JAM ZONE is a GNSS spectrum monitoring system, capable of detecting interferences that affect the proper functioning of these systems.
NO JAM ZONE works with the most-used GNSS frequencies. Because of its distributed network of beacons, the system is able to give, in real time, the position of a source of interference.
NO JAM ZONE can detect the following types of interference:
Service interruption (jamming)
Spoofing interference
Induced electromagnetic emissions whose power exceeds a certain harmful threshold
Centum also offers the NO FLY ZRONE drone and remotely piloted aircraft system (RPAS) detection and protection system. The active defense system is capable of detecting threats and creating a shield of electronic countermeasures preventing the intrusion of drones in protected areas and diverting them to safe catch areas.
NO FLY ZRONE is effective against the vast majority of drones and RPAS, whether radio-controlled by an operator or by autonomous guidance through GPS. It detects drones and discriminates against those that constitute potential threats.
Once the threat has been classified, NO FLY ZRONE neutralizes the drone control system by taking control of it and driving it to a safe area for its descent and capture; it can also geolocate the operator of the drone.
Charles Curry, founder of Chronos Technology, with the RIN award. (Photo: Chronos)
Chronos Technology has been recognized by the Royal Institute of Navigation (RIN) with its 2018 Duke of Edinburgh’s Navigation Award for Outstanding Technical Achievement in “recognition of technical excellence and authority in satellite navigation and timing vulnerabilities and mitigations.”
Charles Curry, founder and managing director of Chronos Technology, accepted the award at the RIN Annual General Meeting held June 10 in London.
Chronos has worked with the RIN and others since 2008 to raise awareness of GNSS vulnerabilities, and in collaboration with the University of Bath has developed a family of GNSS interference detection products.
Chronos first started researching this phenomenon with the University of Bath in the Innovate U.K. project GAARDIAN in 2008, closely followed by the SENTINEL and AJR projects. The projects have focused on detecting and locating commercial off-the-shelf jammers mostly sourced from Chinese websites.
The first success was seizing the so called “Girvan Jammer” in 2011, when a jammer was recovered from a commercial van driver through collaboration with the serious and organized crime group of the local police.
This exercise took about two weeks from initial detection to recovery of the jammer and should be compared to the six months it took U.S. enforcement agencies to identify the so called “Newark Jammer.”
SENTINEL sensors were originally rolled out in 2010 continue to provide real evidence of jamming at various locations around the U.K. The project assists police work by collating jamming events by day and time of day using a cloud-based portal.
The GPS interference detection portfolio includes low-cost, handheld GNSS interference detectors with features such as data logging and direction finding capabilities to precisely pinpoint a jammer.
The latest product to emerge is known as “JammerCam,” and is the first GPS jamming detector in the world to be able to take photographs of a moving vehicle, which is carrying a GPS jammer. This is now in trials with various local police forces and is photographing vehicles with jammers on a daily basis, providing real-time actionable intelligence to the law enforcement officers’ smartphones identifying vehicle type, color and number plate.
Early trials with this research are leading to the seizing of at least one jammer per week by U.K. law enforcement agencies.
Chronos has demonstrated the ability to work with universities and potential users to develop new, affordable products to meet a genuine need. Customers include U.K. and international law enforcement agencies and military users.
“This is a very prestigious award, as a look at the previous winners will attest,” said John Pottle, director of the Royal Institute of Navigation. “Chronos is very well respected and has continued to innovate, achieving global influence from their U.K. base. Many congratulations to all at Chronos for this well-deserved recognition.”
Curry was awarded Fellowship of the Royal Institute of Navigation in 2016 in recognition of his significant and continuing contribution to the practical aspects of time measurement and dissemination, including research into GNSS vulnerabilities and the use of eLoran for precise time.
“Chronos is honored to be the recipient of the RIN’s 2018 Duke of Edinburgh’s Navigation Award for Outstanding Technical Achievement,” Curry said. “We could not have done this without our close association with the University of Bath, in particular Dr. Robert Watson and Professor Cathryn Mitchell and their colleagues in the Electrical & Electronic Faculty.
Over the years, this association has enabled Chronos to undertake research and bring to the market GPS jamming detection products which have been thoroughly field tested at locations such as Sennybridge in the Brecon Beacons, and other international jamming trials in Norway and the U.S.
“In particular, Chronos was the only British company to be invited to JamX17 in Idaho Falls, U.S., by the U.S. Department of Homeland Security to showcase the detectors’ technology,” Curry said.
Eight years on, and the money generated by a hedge fund still seeks to destabilize the Global Positioning System and the billions of people who benefit from it, whether they create it, administer it, sell it or use it.
That accounts for just about everybody touched by telecommunications and the industrial network, including the brave individuals serving in the military.
For rewards to the few, the U.S. government is lobbied to levy hardship on the many. In the service of that lobbying, the truth is manipulated to suit the ends. Sound familiar?
See this article for facts and findings of years of detailed study of this matter, recounted in the recently released Department of Transportation’s Adjacent Band Compatibility (ABC) Assessment Final Report. That it appears almost a year after all the research and nearly all the analysis was completed suggests that powerful forces are at work, perhaps warring with each other, within the government. Let us hope that the guys and gals with the good hats on can prevail.
The matter now rests with the Federal Communications Commission, an appointed panel not subject to the electorate nor to Congress, whose decisions sometimes carry a scent of influence from hidden quarters. Note particularly the recent ruling against net neutrality, which most agree runs counter to public interest and correlative with private corporate interests.
As our news story states, the ability to use the mobile satellite services (MSS) band for terrestrial services is “limited.” The DOT ABC Assessment, using the 1-dB Interference Protection Criterion of a 1-dB drop in carrier-to-noise density ratio that’s accepted by everybody — specifically, every engineer — on Earth except Ligado Networks, demonstrates conclusively there is no chance the company could actually deploy a viable commercial terrestrial service in the MSS spectrum without disrupting or degrading vital GPS and GNSS services.
The U.S. government and the GPS industry have both expended far too much money and time trying to accommodate a force-fit of a non-compatible use into the bands adjacent to the radionavigation satellite service band, to the benefit of one commercial company’s bottom line.
It’s high time for this nonsense to stop. Nobody, least of all the government and the GNSS industry, owes Ligado anything.
By Paul Alves, Carmen Wong, Matthew Clampitt, Eric Davis and Eunju Kwak
INNOVATION INSIGHTS with Richard Langley
WE LIVE IN A POLLUTED WORLD. Sometimes even pristine environments are desecrated.
No, I’m not talking here about the rubbish on Mount Everest, nor the leaching of heavy metals from tailing ponds, nor the plastic trash in the oceans, nor the sulfur dioxide in the atmosphere.
I’m talking about radio-frequency pollution. Just as we would like to have our physical environment free of pollution for our better health and that of the ecosystem, we would like the radio spectrum to be free of pollution so that its users — virtually everyone on the planet — can have a better RF experience, whether it be when listening to the radio, using a cell phone or operating a GNSS receiver. We usually call RF pollution interference, or RFI for short, as it interferes with the signal we are trying to receive.
RFI can be accidental or deliberate, in which case we call it jamming. As a shortwave radio enthusiast, I am familiar with both types of RFI. Although the majority of the world’s radio stations attempt to coordinate their broadcasts to ensure that two stations don’t try to beam their signals to a particular area on the same or an adjacent frequency at the same time, it does happen, ruining reception. And if a country doesn’t want its citizens listening to certain foreign radio broadcasts, it might attempt to jam them as the Soviet Union did in the past and as China, North Korea, Cuba and several other countries still do.
In this month’s column, we look at GNSS interference. In many cases, GNSS interference is accidental, with a nearby radio device putting out a signal at a fundamental frequency or a harmonic, which lies within the passband of one of the GNSS frequencies.
It could be intentional, too, and we’ve all heard about GPS jammers including the so-called personal privacy devices that deliberately interfere with GPS signal reception. Is there any way to detect GNSS interference and to find its source so that remedial action can be taken? Yes and yes. A team of authors from NovAtel tell us how.
Interference is a growing concern among GNSS users, particularly in parts of the world where radio frequency transmission is not strictly regulated. Intentional interference and jamming is cheap and relatively easy to obtain in the form of personal privacy devices (PPDs). These devices can sometimes cause unintended interference and jamming to important infrastructure such as an airport. In this article, we describe a method for creating an interference map using the NovAtel OEM7 Interference Tool Kit (ITK). The ITK is capable of detecting and eliminating interference, and can be used to measure the power of a received interferer. When data is collected for an area around a static and continuously operating interference source, it can be used to map out the interference over the affected area. We overview a method for mapping the interference and, using a model of power loss over distance, creating a map of the interferer’s likely position. We also discuss simulated results and three case studies with live (real-data) interference sources from India, Canada and Japan.
NovAtel introduced the ITK in 2016. The ITK’s interference detection provides a list of sources, which includes an estimate of the frequency, bandwidth and power of the measured interference. It also provides the power levels across the entire frequency band of the front end. Either of these can be used as measurements of the received interference power levels. When the power levels for a given frequency are combined from multiple locations, they can be used to estimate the power and location of the interference source. The received power levels can also be combined to estimate the interference power as a function of location. The performance degradation experienced by one receiver at a given interference level can be extrapolated to other receivers at the estimated interference levels.
INTERFERENCE DETECTION
The ITK tools include the ability to visualize the power received across the input frequencies (front-end) bands. This can be used to quickly and easily identify any irregularities in the spectrum. These irregularities could be caused by internal interference, which is interference between electrical components introduced through hardware integration or installation. It can also be caused by external interference, such as by a PPD or other nearby radio transmitter.
The ITK’s detection feature identifies potential interference and provides a list of the interference power, frequency and bandwidth. This makes it easier for integrators to automate responses to potential interference without the need to scan the spectrum themselves. FIGURE 1 shows the received signal power and interference detection threshold for the GPS L1 frequency band. In this case there is no interference detected.
FIGURE 1. Received signal power (blue) and interference detection threshold (red) for L1.
The detection threshold is adjustable. However, if it is set too high, it can cause interference to be undetected; if it is set too low, it can cause false detection. For this example, a fairly low value was chosen because we were willing to manually identify the interference source and ignore any false detection.
The ITK also includes tools to mitigate interference, limiting or eliminating its impact. This includes a high dynamic range mode, which is effective in reducing the impact of interference. If this is not sufficient, then notch or low-pass filters also can be applied to completely cut out parts of the spectrum to neutralize the impact of interference or jamming.
FREE-SPACE LOSS
The mapping algorithm, which will be discussed later, requires a model of the power loss as a function of distance (d) to the transmitter. As the wave spreads from the transmission source, the power is lost according to:
(1)
where Lp (dB) is the power loss in dB, d is the distance in meters, and λ is the wavelength in meters. This equation can be expanded into a function of frequency (f, in Hz) and distance (d, in millimeters). Changing the units in this equation changes the constants.
(2)
For example, if the transmitter is broadcasting at 1.237 GHz, then Equation (2) gives
(3)
This ideal power loss is significantly increased by physical obstructions that are common, such as vehicles, buildings, trees or the terrain type. Different materials can have significantly different impacts on the power loss.
Some researchers have used a precomputed power map and map matching for indoor positioning. This method uses the expected received power to position a receiver. The same algorithm that is used to position the receiver could also be used to position the transmitter.
FIGURE 2 shows the received power as a function of distance that was observed for the Calgary test. There is a large variability in the power, likely due to natural obstructions.
FIGURE 2. Received power as a function of distance from the transmitter.
The equation for the line of best fit of this data is significantly different from Equation (3). This is likely due to the obstructions and limited number of data points. Due to problems with inaccuracies with this data fit, any further power calculations will use Equation (2).
MAPPING THE INTERFERENCE IMPACT
Using a single observation of the received interference power, a profile of the transmit power as a function of location can be created using a power decay curve similar to that shown in Figure 2. If we assume that the transmitter is at a given position and use the decay curve through the observed power, then we can estimate the transmit power at that location. When we do this for multiple locations, a power profile is created. This process is shown in FIGURE 3. When these plotted estimates are connected continuously, then we get a power profile.
FIGURE 3. Received power as a function of distance from the transmitter.
This power profile could pertain to a lower power transmitter that is relatively close to the receiving antenna or could be a stronger transmitter that is farther away. A single transmitter at any location could be responsible for the received power depending on the power of the transmitter.
When additional measurement points are added at different locations, the estimated powers of the transmitter for each individual observation can be combined. The estimated transmit power at some of the potential transmitter locations will match between the observations. For potential interferer locations that are far from the true transmitter location, the observations will conflict with each other.
Creating this type of power profile can be useful for pre-analysis. If we assume that none of the measurement locations can observe the interference, then the received interference must be equal to or less than the noise floor. If we assume that the received interference is at the noise floor, then we can use this profile map to identify the power of any hidden, undetectable transmitters in a region. An interferer may be broadcasting under the noise floor, undetectable at that power and distance. For example, if we want to monitor an area for interference around critical infrastructure, such as an airport, then we can deploy a network of ITK receivers. If no interference is detected, it is still possible for interference to be present if the power level of the transmitter is low enough that it does not reach any of the receivers above the noise floor. This analysis can be used to estimate the minimum detectable interference across the area, and used to determine the receiver network spacing and locations to ensure the minimum detectable interference is immediately detected.
FIGURE 4 shows an example of measurement points from the India case study. It shows the estimated power of a potentially undetectable interference source if no interference is detected anywhere at the measurement points. Lighter colors indicate a higher undetectable interference power. Notice how it is possible to miss a weak interferer that is close or a high-powered interference source that is farther away. This also illustrates how much information we can gather from zero-observation points where interference could not be detected.
FIGURE 4. Locations and power of possibly hidden interference sources that would be undetectable by observation points, shown as blue dots (Map data: Google, DigitalGlobe).
This method could be used to determine the path or spacing of receivers to monitor a region to detect interference at a certain level. With some history added into the model so that the uncertainty increased over time, a single receiver or a fleet of receivers could plan out their routes to monitor for interference.
The estimated interference source power can be used to determine the impact of the interference and give an estimate of the location of the interferer. A single static interferer will be assumed when estimating the location of the interferer using a goodness-of-fit model. A grid is created over the interference area. For each point in the grid, the attenuation (power loss) model is used to calculate the residual between the minimum transmit power and all power measurement points. If the residuals are low for all the observed power locations, then this is the most likely location of the interference transmitter.
FIGURE 5. Example of the goodness of fit for potential transmitter location and power.
FIGURE 5 shows an example of this goodness-of-fit test. The red dot shows the location of a potential transmitter location under test. Using the distance attenuation model, the predicted received power for each of the measurement points is calculated. The difference between the expected received power and the actual received power is an indication that this is not the correct transmitter location. The root-mean-square error of the fit error for all the observed points gives a likelihood that the transmitter is at this location.
SIMULATED RESULTS
Using the goodness-of-fit method, we can generate reasonable visualizations of the interference effect. FIGURE 6 shows an example map produced from simulated interference to the east.
FIGURE 6. Interference map from a simulation where the interference is on the east side (Map data: Google).
The expected power attenuation model matches perfectly with the data because it is a simulation. Similar results were obtained when the interference was assumed to come from the west and north. The yellow line shows a “roller-coaster” plot of the interference power. The height of the line shows the relative received power. Notice that it increases as we approach the source of the interference and decreases as the path moves away from the interference. A combination of the roller-coaster plot and the map give a quick visualization of the impact and location of the interference. There is a slight ambiguity between the east and west side of the road because the transmitter is close to the road. The goodness of fit works very well in this case to identify the location of the interference source.
FIGURE 7 shows a case where two interference sources are simulated. In this case, the model breaks down because it assumes that there is only a single interference source. The model clearly has difficulties determining the location of the interference. Even with accuracy issues, the model could still be used as a visualization of the interference that is easier to interpret than looking at numbers in a table.
FIGURE 7. Interference map from a simulation with 2 interference sources (Map data: Google).
INDIA DATASET
This dataset was the initial motivation for this work. A customer reported intermittent tracking problems with a newly installed receiver. The receiver would stop tracking for a few hours every evening. Customer service visited the site to investigate. Because of the intermittent nature of the problem, interference was suspected. An OEM729 receiver was walked around the affected antenna in an attempt to find the source of the interference and also to prove to the customer that interference was in fact the cause of the tracking problems.
FIGURE 8 shows the collected measurements. The numbers shown are the received interference powers at each location. It is possible to approximate the location of the interference and the impacted area by looking closely at the measurements, but it takes some close examination and interpretation.
FIGURE 8. Received interference power measured when searching for interference in India.
The source of the interference was identified using this approach. It was found to be a weather station, which performs a nightly upload of data collected throughout the day. This weather station broadcasts at 1580 MHz, which was jamming L1. The customer was able to move the interfering antenna to another site. The customer also could have used the ITK to apply a notch filter, which would have mitigated the interference’s impact, but it is better to remove the source of interference if possible.
Using the data points collected, an interference map can be generated using the method described. This map is shown in FIGURE 9. The lighter color indicates a higher likelihood that the interference transmitter is at that location. The location of the transmitter is also shown in the figure. The likelihood map is very close to the actual location of the transmitter. It gives a quick and easy-to-interpret visualization as opposed to individual measurement points.
FIGURE 9. Interference map for the India case study (Map data: Google, DigitalGlobe).
CALGARY DATASET
We were made aware of a potential unintentional L2 interference device and took it to Cross Iron Mills mall, north of Calgary, Canada, to investigate. FIGURE 10 shows a map of the area.
FIGURE 10. Map of the test area showing the location of the interference source.
We drove the path shown in blue to characterize the interference, and collected data using an OEM729 receiver with the ITK feature. Two buildings are near the interference source: a smaller building to the north and a large building to the south. These buildings block and shield the receiver from the interference when it is between the interference and the receiver.
The interference device was a transmitter to send video from a drone to a monitor, broadcasting at 1.2 GHz with 800 milliwatts. It was purchased online with no warnings about potential impacts it may have on other systems or devices. As recreational drones (and their electronics) become more popular, unintentional jammers and interference sources could become commonplace. We have no continuous monitoring and enforcement for short-range and short-duration unintentional jammers such as this one.
Although many commercial-grade receivers, such as ones common in cell phone and GPS watches, were unaffected because they only operate at L1, the box the device came in also indicates that there is a 1.5-GHz model capable of broadcasting at 2 watts. With 2 watts at 1.5 GHz, GPS L1 would be significantly jammed. This emphasizes the need for interference detection and mitigation. Nothing is stopping recreational hobbyists from accidentally jamming a significant number of users and services.
FIGURE 11 shows the roller-coaster plot of the interference observed during the test. The height of the yellow bars indicates the received power for the L2 interference. The power is generally higher closer to the interference source and decreases as a function of distance; however, there is a lot of deviation. Physical obstructions also cause significant decreases in received power.
FIGURE 11. Observed power of the interference source (yellow) over the test course (Map data: Google, Landsat / Copernicus, DigitalGlobe).
For example, on the north end of the small building, shown on the right side of the figure, the observed interference power drops to almost zero despite being relatively close to the interference source. The large variations in power throughout the southern loop may be due to partial obstructions from parked cars or outcrops of the building. These physical obstructions cause larger decreases in received power than simply moving the antennas away from each other.
Since the interference was only broadcasting on L2, a position is still available through the other GNSS frequencies. The GPS receiver had difficulty tracking GPS L2 signals because of the interference.
FIGURE 12 shows the number of GPS L2 signals tracked. As the receiver approached the interference source, it became more and more difficult to track the L2 signals. As the receiver moved away from the interference, or behind a physical obstruction (like a building), the impact of the interference decreased and the signals were reacquired.
FIGURE 12. Number of L2 satellites tracked (red) over part of the test course (Map data: Google, Landsat / Copernicus, DigitalGlobe).
This shows how a simple device can inadvertently be harmful. Anyone could have purchased this device to transmit video from their recreational drone. Since this device only broadcasts on L2, the GPS of the drone and many nearby devices would have been unaffected, while almost completely jamming and disrupting any dual-frequency receivers nearby.
FIGURE 13 shows the interference goodness-of-fit map from the real data test. The map shows the correct trend, but the peak of the map does not include the actual location of the interference transmitter. This is due to inaccuracies in the power attenuation model. For example, a significant shift to the south is due to the rapid decrease in power when moving behind the north building.
FIGURE 13. Interference map from the real-data test.
When only the southern dataset is considered, we get a more accurate map, one not impacted by the northern building. This is because the attenuation model does not account for obstructions. The performance of this kind of model could be significantly improved with a model that includes the topography and buildings.
Despite the inaccuracy of the map to precisely locate the interference source, these simple model maps give a nice visualization of the interference.
TOKYO REAL DATA RESULTS
We received a report of interference in Tokyo, Japan, and took a receiver there to investigate. FIGURE 14 shows the maximum received power throughout the dataset. The interference around 1570.69 MHz is obvious and easily to identify in the figure.
FIGURE 14. Spectrum power level for the Tokyo dataset.
FIGURE 15 shows the observed power of the interference source when walking around the building. There is a peak in the received power when moving to one side of the building, while the observed power is relatively constant over the other three sides of the building. This strongly suggests that the interference source is along the one side of the building.
FIGURE 15. Observed power of the interference source (yellow) for the Tokyo dataset (Map data: Google, Zenrin).
This figure also shows the estimated goodness-of-fit interference map produced using the algorithm described earlier. The source of the interference could not be conclusively determined; however, we believe that the source was emanating from one of the vehicles in the parking lot.
This real example illustrates how useful this visualization of the observed power is in understanding the nature of the interference, identifying the source and localizing its effect. The interference in this case did not cause a noticeable change in the number of satellites or signals tracked.
CONCLUSIONS
This article showed a creative and useful application of NovAtel’s Interference Tool Kit available as a feature on the OEM7 line of receivers. The ITK can be used to create maps that show the estimated location of an interferer as well as the impact of the interference on other users. We demonstrated this using simulated datasets where the agreement between the simulated and actual loss-of-power models made for overly optimistic results. Three case studies are also shown: The original motivation for this work was a customer-service case in India. The second is a case in Calgary where unintentional interference was being caused by a drone video transmitter. The third dataset from Tokyo was a similar example, where, unfortunately, the true interference source could not be conclusively identified.
The three interference case studies show the importance of interference detection and mitigation because intentional and unintentional interference sources are easy to obtain and are not easily monitored or restricted. In one of these cases, a device that was naively purchased online as a UAV video transmitter ended up jamming GPS L2 in an area of roughly 2,000 square meters. With interference mitigation, it is possible to continue to work and operate in these environments without interruption or significant impact.
ACKNOWLEDGMENTS
The authors thank Bryan Leedham and Saravanan Karuppasamy for sharing their customer stories with us and providing us with the data for the case studies. This article is based on the paper “Interference Likelihood Mapping with Case Studies” presented at ION ITM 2018, the 2018 International Technical Meeting of The Institute of Navigation, Reston, Virginia, Jan. 29–Feb. 1, 2018.
Paul Alves received a Ph.D. from the Department of Geomatics Engineering at the University of Calgary in 2006. He is a principal research engineer in the Applied Research Team at NovAtel Inc. in Calgary, Canada.
Carmen Wong is a geomatics engineer at NovAtel. She received her B.Sc. in geomatics engineering with biomedical specialization from the University of Calgary in 2008.
Matthew Clampitt graduated in 2014 with a B.Sc. in geomatics engineering from the University of Calgary and is now a developer in the Positioning Algorithms Group at NovAtel.
Eric Davis has an undergraduate degree from the University of Calgary, with majors in both astrophysics and physics. He also earned an M.Sc. in physics at the University of Calgary. He joined NovAtel in 2016.
Eunju Kwak received her Ph.D. from the Department of Geomatics Engineering, University of Calgary, in 2013. She is a geomatics engineer at NovAtel.
FURTHER READING
• Authors’ Conference Paper
“Interference Likelihood Mapping with Case Studies” by P. Alves, C. Wong, M. Clampitt, E. Davis and E. Kwak in Proceedings of ION ITM 2018, the 2018 International Technical Meeting of The Institute of Navigation, Reston, Virginia, Jan. 29–Feb. 1, 2018, pp. 467–482.
• GNSS Interference and Jamming Detection
“Interference” by T. Humphreys, Chapter 16 in Springer Handbook of Global Navigation Satellite Systems, edited by P.J.G. Teunissen and O. Montenbruck, published by Springer International Publishing AG, Cham, Switzerland, 2017.
“Demonstrated Interference Detection and Mitigation with a Multi-frequency High Precision Receiver” by F. Gao and S. Kennedy in Proceedings of ION GNSS+ 2016, the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, Sept. 12–16, 2016, pp. 159–170.
“Signal Acquisition and Tracking of Chirp-Style GPS Jammers” by R.H. Mitch, M.L. Psiaki, S.P. Powell, and B.W. O’Hanlon in Proceedings of ION GNSS+ 2013, the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, Sept. 16–20, 2013, pp. 2893–2909.
• Radio Frequency Propagation Radio Frequency Propagation Made Easy by S. Faruque, SpringerBriefs in Electrical and Computer Engineering, published by Springer International Publishing AG, Cham, Switzerland, 2015.
• Localization Based on Signal Power
“Indoor Localization Based on Floor Plans and Power Maps: Non-Line of Sight to Virtual Line of Sight” by J.J. Khalifeh, Z.M. Kassas and S.S. Saab in Proceedings of ION GNSS+ 2015, the 28th International Technical Meeting of the Satellite Division of The Institute of Navigation, Tampa, Florida, Sept. 14–18, 2015, pp. 2291–2300.
The culmination of several years of test and analysis conducted by the U.S. Department of Transportation, the assessment will play a key role in the Federal Communications Commission’s upcoming decision on a proposal from Ligado Networks.
The long-awaited Final Report for the U.S. Department of Transportation’s Adjacent Band Compatibility (ABC) Assessment was released on April 26.
The report is the culmination of several years of test and analysis conducted by the DOT, with input and assistance from the public and federal agency stakeholders. Though not explicitly motivated by it, the assessment appears to be responsive to the Positioning, Navigation, and Timing (PNT) Executive Committee’s (EXCOM’s) Jan. 13, 2012, memorandum to the National Telecommunications and Information Administration (NTIA) that sought to develop metrics to inform commercial non-space proposals for use of frequency bands adjacent to those used by GPS, so that existing and evolving space-based PNT services “vital to economic, public safety, scientific and national security needs” were not affected by implementing such proposals.
The assessment will likely play a key role in the Federal Communications Commission’s upcoming decision on a proposal from Ligado Networks to add an extensive complex of powerful ground transmitters to its system, broadcasting on frequencies allocated for satellites.
Open and Transparent. Two key attributes of the ABC assessment were that it was conducted openly and transparently, with numerous public workshops announced via the Federal Register, and it was agnostic to any particular proposal for use of bands adjacent to GPS/GNSS services. The approach chosen by DOT in performing its assessment was to develop maximum tolerable effective isotropic radiated power (EIRP) levels that could be transmitted at differing frequency offsets from the GPS L1 center frequency.
The term “adjacent” in this regard is a bit of a misnomer in that the assessment range extended to 100 MHz on either side of the GPS L1 center frequency of 1575.42 MHz. This approach was recently validated by the National PNT Systems Engineering Forum (NPEF), which found the ABC assessment was the only one of five test and analysis efforts conducted since 2011 on adjacent-band terrestrial operations that met all six of the test criteria recommended by the experts serving on the National PNT Advisory Board. The NPEF analysis is available here.
Measurements on 80 civil GNSS and GPS receivers were performed at White Sands Missile Range (WSMR) in New Mexico. The Air Force conducted a prior week of testing on military GPS receivers at WSMR, and while the results of that testing are classified, an Air Force briefing at the November 2017 PNT Advisory Board meeting indicated the military receiver test results supported the conclusions drawn by the DOT ABC assessment. Certified aviation GPS/GNSS receivers were analyzed by RTCA Inc. and are being analyzed by the FAA in terms of determining power levels in adjacent bands that don’t exceed FAA Technical Standard Orders. However, the overall ABC assessment indicates that certified aviation receivers are not the limiting case for tolerable interference from adjacent-band services to GPS and GNSS receivers.
Test Procedures
Compatibility assessment for the civil receivers consisted of conducting the initial measurements at WSMR for six categories of receivers: aviation (non-certified), cellular, general location/navigation, high-precision, timing, and space-based receivers. These were evaluated to determine what DOT called Interference Tolerance Masks (ITMs) for each category of receiver and each receiver tested. The ITMs define the maximum aggregate interfering power that can be tolerated by a given GPS or GNSS receiver. The ITMs are based on the internationally accepted Interference Protection Criterion (IPC) of a 1-dB drop-in carrier-to-noise density ratio (C/NO) for the receiver, or, equivalently, an interference density-to-noise ratio (IO/NO) of –6 dB. This 1 dB IPC standard, which NTIA directed to be used in the NPEF evaluation of the original LightSquared (now Ligado) adjacent-band proposal in late 2011, is explained in great detail in a white paper the Air Force made publicly available in 2017.
The assessment then developed, with input from the public at several workshops convened by the DOT, use cases to determine how close a receiver for a particular GPS or GNSS application might be to a base station or handset of a commercial terrestrial service in an adjacent band. Proximity distances of 10 and 100 meters were selected from these use cases, and maximum tolerable transmit EIRP levels for a given frequency offset were determined; see Figure 1. The high-precision receivers (HPRs) were the most susceptible to interference from terrestrial operations in the adjacent bands.
Figure 1. Maximum tolerable power level for GPS/GNSS receivers at 1530 MHz. (Table: DOT)
One thing that seems clear is that, with tolerable transmit power levels in the milliwatt and microwatt range, the potential to use the bands near GPS frequencies for commercial terrestrial wireless services may be limited. Illustrating that point further, the assessment shows that, based on the assumptions in the study, HPRs can be affected at distances beyond 14 kilometers (see Figure 2), and that loss of lock for low-elevation satellites can occur at distances of up to 3 kilometers from a base station providing terrestrial services using characteristics adopted internationally in the International Telecommunication Union (ITU) study groups.
Figure 2. Impact of a 29-dBW cellular base station transmitting at 1530 MHz on a high-precision GPS/GNSS receiver. (Chart: DOT)
Moreover, the assessment determined that the potential interference to other GNSS systems may be more problematic, noting that “the levels that protect all GNSS signals can be as much as 15 dB lower than those needed to protect L1 C/A signals from base station emissions with an average difference of 3.5 dB across all frequencies and five categories considered.”
Galileo’s Role. Since 2013, according to a Public Notice from the FCC, the European Commission has sought a waiver of FCC rules that require licensing of receivers operating with foreign satellites so that Galileo service can be provided in the United States. The FCC has yet to act on this waiver request, which was issued in a January 2017 Public Notice, despite overwhelming public support and a positive recommendation from the Executive Branch in 2015.
Figure 3. Bounding masks for each category corresponding to the 10 MHz LTE interference signal and L1 C/A GPS signal: general aviation, general location and navigation, high precision, timing, space-based, cellular. (Graph: DOT)
Conclusions
It is well known that all receivers take in some power from signals transmitted in nearby frequency bands. Considering this fact, the ABC assessment is relatively unique in that it examines the overall spectral environment in which GPS/GNSS operations can be affected rather than just the band allocated to the Radionavigation-Satellite Service (RNSS, the broad radiocommunication service defined in the ITU and in domestic rules under which GPS and other GNSS systems operate) between 1559–1610 MHz. That the overall environment should be considered is an important aspect of any discussion of protecting GPS and other GNSS services given the U.S. National Space Policy that was signed into effect June 28, 2010, that directs the U.S. government to “take necessary measures to sustain the radiofrequency environment in which critical U.S. space systems operate.” This policy is still in effect, and it would be difficult to argue that GPS is not a critical U.S. space system.
Recently, the reconstituted National Space Council adopted four recommendations, one of which related to spectrum used for satellite services and said that NTIA should coordinate with the FCC to ensure “the protection and stewardship of radio frequency spectrum necessary for commercial space activities.” Stewardship that is consistent with National Space Policy would include sustaining the RF environment for GPS.
As the PNT EXCOM has made clear, GPS is “vital to economic, public safety, scientific, and national security needs” of the U.S. Moreover, economic analysis presented to the PNT Advisory Board in 2015 estimated the economic benefit to the nation of GPS services at over 68 billion dollars annually. With the release of the ABC assessment, definitive information is now available to inform decisions on use of frequencies near those used to provide space-based PNT services so these critical services are not disrupted or degraded.
GNSS receiver manufacturer Septentrio is introducing its AsteRx SB at two industry shows: Expomin in Santiago, Chile (April 23-27), and Intermat in Paris, France.
According to the company, the AsteRx SB delivers Septentrio’s quad-constellation real-time kinematic (RTK) positioning in a low-power, IP68 compliant housing. Built around the AsteRx-m2 GNSS receiver engine, the AsteRx SB features Wi-Fi, Bluetooth, USB, Ethernet and serial connectivity.
Septentrio’s GNSS+ suite of positioning algorithms converts difficult environments into good positioning: LOCK+ technology to maintain tracking during heavy vibration, APME+ to combat multipath, and IONO+ technology to ensure position accuracy during periods of elevated ionospheric activity.
The AsteRx SB also features the AIM+ interference mitigation and monitoring system, which can suppress the widest variety of interferers, from simple continuous narrowband signals to the most complex wideband and pulsed jammers.
AIM+ interference monitoring and mitigation system
L-band PPP, RTK, scalable accuracy
High-update rate, low-latency positioning
Base and rover operation
Bluetooth, Wi-Fi, Ethernet, serial and USB communications
Whether exposed to the elements or inside a vehicle cab, operating alone or as a core component of a sensor-fusion system, the AsteRx SB is straight-forward to set up and integrate into any new or existing application. Using Wi-Fi or micro USB, the AsteRx SB can be configured and monitored using any device with a web browser.
“We believe the AsteRx SB is the best all-rounder on the market today. We’ve produced a small and low-power device with zero compromise on performance,” said Gustavo Lopez, product manager at Septentrio. “From machine control to sensor-fusion applications, manned or unmanned, the compact size and low power of the AsteRx SB along with its range of communications options make it ideal for any project requiring reliable high-precision positioning.”
At Intermat in Paris, Septentrio will exhibit at Booth 6H-041 and at Expomin in Santiago, Chile, at Booth 1K-30.
Septentrio debuted the AsteRx-m2a and AsteRx-m2a UAS GNSS OEM engines at Commercial UAV 2017, held Oct. 24-26 in Las Vegas.
The two new OEM boards provide precise and reliable multi-frequency, all-in-view real-time kinematic (RTK) positioning and heading — along with interference technology — with low power consumption, the company said.
Both boards are smaller than a credit card and feature Septentrio’s AIM+ interference mitigation and monitoring system. AIM+ can suppress a wide variety of interferers, from simple continuous narrowband signals to the most complex wideband and pulsed jammers.
The AsteRx-m2a board by Septentrio. Photo: Septentrio
Increasing levels of radio-frequency pollution, coupled with the intrinsic danger of self-interference in compact systems such as UAS, makes interference mitigation a vital element in any UAS system that uses GNSS positioning.
Both boards are designed to bring high-precision positioning and attitude to any space-constrained application. According to the company, both receivers are designed to serve as core components in any multi-sensor application.
The AsteRx-m2a UAS is aimed specifically at unmanned applications, bringing plug-and-play compatibility for autopilot systems such as ArduPilot and Pixhawk. Event markers accurately synchronize camera shutter events with GNSS time. The board can be powered directly from the vehicle power bus via its wide-range input.
The AsteRx-m2 UAS board by Septentrio. Photo: Septentrio
The AsteRx-m2a UAS works seamlessly with GeoTagZ software, providing offline re-processed RTK accuracy without the need for either ground control points or a real-time datalink.
“We’ve taken the hugely successful AsteRx-m2 and added a second antenna input for high-precision GNSS heading,” said Gustavo Lopez, OEM product manager at Septentrio. “No need to manoeuvre around in a figure of ‘8’ trying to initialise INS heading or find space or additional power for a separate INS module now. All you need is a second antenna and you’re good to go.”
Septentrio is located at booth 206 of Commercial UAV Expo 2017.
