Systems and methods for GNSS jammer detection and localization using ADS-b data

Abstract

An air traffic surveillance and management system is disclosed that includes a data recordation module for recording avionics data from aircraft in a region of interest, the avionics data including signal quality information and aircraft location information, a threshold-crossing and filtering module for processing the avionics data to identify a filtered set of falling changes in signal quality information falling below a threshold signal quality value, and for processing the avionics data to identify a filtered set of rising changes in signal quality information rising above the threshold signal quality value, collectively filtered threshold-crossing data, a clustering module for identifying filtered threshold-crossing data associated with any of spatial or temporal clusters, and an action module that identifies a jamming interference region based on the threshold-crossing data associated with any of spatial or temporal clusters.

Description

SYSTEMS AND METHODS FOR GNSS JAMMER DETECTION AND LOCALIZATIONUSING ADS-B DATAPRIORITY

[0001] The present application claims priority to U.S. Provisional Patent Application 63 / 536,947 filed September 7, 2023, the disclosure of which is hereby incorporated by reference in its entirety.BACKGROUND

[0002] For many decades, radar had long been the dominant technology used in air traffic surveillance and management, but over the last two decades as Global Navigation Satellite Systems (GNSS) such as Global Positioning Systems (GPS) has become fully available for civilian use, this has changed significantly. Today, a GPS-based surveillance technology called Automatic Dependent Surveillance-Broadcast (ADS-B) is widely used in air traffic surveillance and management. Essentially, aircraft equipped with ADS-B utilize GNSS for their positioning and then relay their positional data and other status information to any nearby receiving system. Such GNSS systems have become widely used for many applications including air traffic surveillance and management.

[0003] With the wide-spread use of GNSS systems however, also come vulnerabilities as it has increasingly become a target of nefarious actors who aim to disrupt both civil and military operations. Enabled by the lack of security and resilience in most GNSS, criminals, terrorists and state actors use intentional radio-frequency interference (RFI) to avoid being tracked, to cause economic damage, or to disrupt an opponent’s operations. As a consequence, GNSS RFI, also known as GNSS jamming, has become a growing concern all over the world. In the United States, the President signed an executive order calling for a more responsible use of position, navigation and timing (PNT) services to strengthen national resilience (U.S.Government, 2020), while the European Union Aviation Safety Agency (EASA) has issued a Safety Information Bulletin (SIB) that warns authorities and airspace users about intensified GNSS jamming activities in the context of the Russian invasion of Ukraine (EASA, 2022).

[0004] Typically, the monitoring of GNSS interference is conducted by authorities through the use of ground-based installations or vehicles equipped with direction-finding devices. However, these methods have limitations. Ground-based sensors, for instance, are limited in their detection range to tens of kilometers due to terrain and the curvature of the Earth. Additionally,in the case of short-lived jamming events, authorities may not be able to react quickly enough to move the necessary equipment to an optimal location to locate the source of the interference.As a result, there is a need for approaches that provide continuous monitoring and data collection over large areas, which traditional methods are unable to accomplish.

[0005] The detection of ADS-B signals that are provided by aircraft have long been used for flight surveillance. For example, U.S. Patent No. 11,361,667 discloses the detection of frequency of arrival of ADS-B signals for flight surveillance, cyber security and meteorology. In ADS-B, aircraft continuously report their GNSS position along with a GNSS Information Quality Indicator (IQI) value, such as a navigation integrity category (NIC) code, a navigation accuracy category (NAC) code or the lesser used Navigation Uncertainty Category (NUC) code, indicating the reliability of the transmitted information. When the aircraft is exposed to jamming, the IQI value decreases, which indicates a GNSS degradation or even a GNSS denial. Therefore, by means of the IQI value, ground-based ADS-B receivers can infer information about the GNSS signal quality at the aircraft remotely and, by doing so, use ADS-B -equipped aircraft as flying GPS interference sensors.

[0006] While the idea of using ADS-B data to detect GNSS RFI has also been proposed for both detecting GNSS RF and identifying jammer locations, existing techniques have not been sufficiently effective. Detection methods generally involve either monitoring NIC code or NAC code values, or identifying gaps in ADS-B tracking. Localization methods generally involve either monitoring signal strength or determining centroid calculations. Jammer localization methods in the first category aim to translate NIC values into estimated jamming signal levels at the aircraft antenna, subsequently using this estimation to pinpoint the jammer’s location. Nonetheless, accurately modeling the strength of a signal that originates from the ground and is received by a directed GPS antenna on top of an airborne aircraft is extremely challenging. Several unknown factors, including the transmitter and receiver antenna gain patterns, jamming signal attenuation by the aircraft’s fuselage, among others, play a significant role in influencing the received signal level. Predicting these myriad factors accurately is close to impossible without knowing the exact installations on each aircraft. The situation is further complicated when the parameter used for inferring signal levels (e.g., NIC or NAC) only has 12 distinct levels and frequently simply drops to zero during interference events. Such complexities suggest that the precision and practicality of these methods in real-world scenarios remain notably limited.

[0007] The second category of jammer localization methods focuses on finding a centroid of the affected region to try to estimate the jammer’s location. This approach proves effective in scenarios where the interference area is circular and many aircraft enter the interference areafrom various directions, thus providing a clear picture of the situation. Its efficacy, however, diminishes significantly in situations characterized by low traffic volume, uneven distribution of the traffic, and incomplete coverage of the affected area. Unfortunately, these limitations render these methods unusable in many real-world situations.

[0008] Further, such existing methods have built-in assumptions that limit their applicability in practice. For instance, approaches that rely on signal strength models are susceptible to noise introduced by unknown factors such as signal attenuation by the aircraft’s fuselage, the unknown gain pattern of the GNSS antenna on top of the aircraft, and other factors that affect signal strength or the NIC value itself. Similarly, methods that rely on centroid calculations have problems in scenarios with uneven traffic distributions and sparse data in regions with low traffic volumes.

[0009] There remains a need therefore for improved GNSS jamming detection and jammer localization systems that are able to use small amounts of data and improve operational efficiency and safety of air traffic management and surveillance systems.SUMMARY

[0010] In accordance with an aspect, the invention provides an air traffic surveillance and management system is disclosed that includes a data recordation module for recording avionics data from aircraft in a region of interest, the avionics data including signal quality information and aircraft location information, a threshold-crossing and filtering module for processing the avionics data to identify a filtered set of falling changes in signal quality information falling below a threshold signal quality value, and for processing the avionics data to identify a filtered set of rising changes in signal quality information rising above the threshold signal quality value, collectively filtered threshold-crossing data, a clustering module for identifying filtered threshold-crossing data associated with any of spatial or temporal clusters, and an action module that identifies a jamming interference region based on the threshold-crossing data associated with any of spatial or temporal clusters.

[0011] In accordance with another aspect, the invention provides a method of providing surveillance and management in an air traffic surveillance system. The method includes recording avionics data from aircraft in a region of interest, the avionics data including signal quality information and aircraft location information, processing the avionics data to identify falling changes in signal quality information falling below a threshold signal quality value and to identify rising changes in signal quality information rising above the threshold signal qualityvalue, the falling changes in signal quality information falling below the threshold signal quality value and the rising changes in signal quality information rising above the threshold signal quality value being collectively referred to as threshold-crossing data, analyzing the threshold-crossing data from the threshold-crossing module to detect temporal or spatial clusters of threshold-crossing data, and identifying a jamming interference region based on the clusters of threshold-crossing data.

[0012] In accordance with a further aspect, the invention provides an air traffic surveillance and management system that includes a data recordation module for recording avionics data from aircraft in a region of interest, the avionics data including signal quality information and aircraft location information, a threshold-crossing and filtering module for processing the avionics data to identify a filtered set of falling changes in signal quality information falling below a threshold signal quality value, and for processing the avionics data to identify a filtered set of rising changes in signal quality information rising above the threshold signal quality value, collectively filtered threshold-crossing data, a horizon identification module that generates aircraft horizon data representative of aircraft radio horizons associated with the filtered threshold-crossing data, and an action module that identifies a location of a jamming device based on spatial overlap of the aircraft horizon data.BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The following description may be further understood with reference to the accompanying drawings in which:

[0014] Figure 1 shows an illustrative diagrammatic plan view of a region of interest with identified locations of NIC values illustrated thereon;

[0015] Figure 2 shows an illustrative diagrammatic view of reported altitudes of aircraft reporting NIC values during a limited time period;

[0016] Figures 3A - 3C show process steps of a methodology of practicing an aspect of the present invention;

[0017] Figures 4A and 4B show illustrative diagrammatic views of aircraft proximate a curvature of the earth (Figure 4A) and showing a possible radio horizon of a jamming device (Figure 4B);

[0018] Figures 5A and 5B show illustrative diagrammatic views of aircraft proximate a curvature of the earth (Figure 5A) and showing another possible radio horizon of a jamming device where the radio horizon is shaped by natural or human-made obstructions (Figure 5B);

[0019] Figure 6 shows an illustrative diagrammatic top view of a region of interest showing a plurality of aircraft within and outside of a jamming interference region;

[0020] Figure 7 shows an illustrative diagrammatic side view of a jamming interference region showing the Earth as flat such that the interference region is shown curved;

[0021] Figure 8 shows an illustrative diagrammatic top view of a plurality of radio horizons in a system in accordance with another aspect of the present invention;

[0022] Figure 9 shows an illustrative diagrammatic view of a processing system for use in accordance with an aspect of the present invention including several supporting modules;

[0023] Figure 10 shows an illustrative diagrammatic top view of a plurality of radio horizons in a system in accordance with another aspect of the present invention;

[0024] Figure 11 shows an illustrative diagrammatic top view of a density map of the intersections of the radio horizons of Figure 10; and

[0025] Figure 12 shows an illustrative diagrammatic view of an air traffic surveillance and management system in accordance with an aspect of the present invention.

[0026] The drawings are shown for illustrative purposes only.DETAILED DESCRIPTION

[0027] The invention provides systems and methods that use ADS-B data for the detection and localization of large-scale GPS jamming attacks that does not require full coverage of an attack area yet continuously and promptly detects these threats over expansive regions. More specifically, in accordance with certain aspects, new robust detection methods are disclosed as well as localization methods that use the ADS-B-based observations to reconstruct the GPS jammer’s radio horizon and then uses this information to determine its location.

[0028] In response to the limitations of existing methods, a novel approach is disclosed herein that combines unique ADS-B data processing techniques with radio horizon calculations to locate the source of interference. Unlike previous methods that make assumptions about signal strength models or perform calculations on the shape of the affected region, the approaches disclosed herein first identify the locations where the aircraft intersect the radio horizon of the jammer. By calculating the intersections of these horizons observed from multiple aircraft, the system may determine the location of the jammer. In contrast to existing methods, the process works well even in scenarios with sparse data, when the shape of the affected area is not circular or unknown, and when the interference event is short-lived. Since the method does not make any assumptions about the jammer’s unknown transmit power or the implementation-specific relationship between the IQI values (e.g., NIC values) and the jamming signal strength, the method is widely applicable and robust.

[0029] To detect GPS interferences in ADS-B data, systems and processes in accordance with aspects of the invention rely on the NIC values reported by the avionics. The novelty of the approach involves, in part, how the system processes and filters the NIC values to detect GPS interference. Although ADS-B offers features that can be used for interference detection, such as gaps in the trajectory as well as IQI values, such as the NAC data and NIC data, systems and methods in accordance with certain aspects of the invention use the IQI values such as the NAC data or the NIC data as it can be tracked easily and provides high sensitivity with a comparably low level of noise.

[0030] Figure 1 shows readings of NIC values in an area between Denmark and Sweden connecting the Baltic Sea to the North Sea. Certain readings with the low NIC values (e.g., below six) are shown (using a “+” symbol) at 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, and high NIC values are shown (using a “o” symbol) at 101, 103, 105, 107, 109, 111, 113, 115, 117, 119, 121. Figure 2 shows reported altitudes of aircraft present during the event. Although aircraft ceased reporting their position during the event, they continuously provided their barometric altitude through the interference event. Figure 1 shows a top view and Figure 2 shows a side view, together showing the three-dimensional aspect of the radio horizons. While many of the readings were at higher elevations as shown at 130, some readings were at lower elevations as shown at 132, 134. Both Figures 1 and 2 show data from one minute before the event began to one minute after the event ended.

[0031] In accordance with various aspects of the invention, the system monitors the IQI values such as NIC values or NAC values of all ADS-B-equipped aircraft within a system range, e.g., of one, five, ten or more million square kilometers. When an IQI value drops below a threshold (of e.g., five, six or seven for a NIC value or a NAC value) the system flags the event as an anomaly and creates an entry in its NIC anomalies database. Since these values may typically be between seven and ten, the threshold may be chosen to provide a desired balance between sensitivity and the occurrence of false alarms. Each IQI anomaly (e.g., NIC or NAC anomaly) entry includes the timestamp of the event and other aircraft state information including its position. Once the IQI value of the aircraft recovers, the database entry is updated by adding the same information, marking the end of the anomaly. These anomalies are then post-processed to eliminate noise in the data and to identify potential interference events. Further details on each of these steps are provided below.

[0032] The spatial distribution of irregularities has been found in at least one study to be widely scattered in certain applications, which indicates that factors other than interference may be causing the IQI anomalies. Delving deeper into the spatial density of the anomalies, it has been found that a significant proportion of them were isolated incidents. By additionally considering the temporal distribution of these anomalies, this idea was reinforced by the fact that most of them were not only isolated in space but also in time. It may be hypothesized that if interference were prevalent in a region, it would affect a multitude of aircraft rather than a single one; yet, the density analysis largely rejects this hypothesis for the majority of the anomalies as they appear to be brief IQI fluctuations experienced by individual aircraft, with no surrounding aircraft confirming the disturbance. This observation underscores the need for refined filtering in order to leverage these IQI anomalies for interference detection.

[0033] Further analysis of the data in the study revealed several causes for anomalies unrelated to interference. A significant proportion of the anomalies, about 80%, were brief isolated dropouts lasting less than sixty seconds. These could not be associated with any other nearby anomalies that occurred within a short period of time of each other. These are classified herein as transient avionics disturbances. This abundance of glitches emphasizes a principal drawback of ADS-B, which is its reliance on numerous avionic components. Contrary to radar, an ADS- B-based air traffic surveillance system bears a heightened risk of malfunction due to the increased number of points of failures in the system. The substantial number of anomalies observed further highlights this issue of ADS-B as an air surveillance system.

[0034] Beyond isolated drop-outs, for example, several aircraft consistently exhibited NIC values fluctuating above and below six. This pattern created a trail of anomalies throughout the monitoring area. Although the precise cause remains unidentified and may vary among the aircraft, it was assumed that external interference is not a contributing factor. This is drawn from observing the behavior of these aircraft regardless of their location. Potential causes of such issues might include onboard installation issues like damaged cables, antenna or electronics defects, or incompatibilities between avionic components.

[0035] A less prevalent, yet still misleading source of noise was tied to consecutive NIC anomalies from specific aircraft within limited airspace segments. Such anomalies are often rooted in aerobatics, military training, or, more generally, aircraft undertaking severe aerial maneuvers. The extreme pitch and roll angles of these movements misalign the GPS antennas, typically positioned on the aircraft’s top, away from the satellites. Further blocked by the aircraft’s fuselage, these antennas lose their direct satellite line of sight in some directions andelevation angles. This results in a degradation of the IQI value which in turn results in a sequence of IQI anomalies for that aircraft.

[0036] With reference to Figure 3 A, the system begins (step 1000) by identifying the area of interest, for example a flight information region, and assigning a region of data collection. To harness IQI anomalies for GNSS interference detection, it’s crucial to filter out the aforementioned noise. The system therefore applies the following filtering steps either before or after the collection of threshold-crossing data as discussed herein. The process both identifies locations of aircraft when each aircraft registers an IQI value crossing a threshold (either rising above the threshold or falling below the threshold (step 1002). The process also filters the data (again either before or after the threshold-crossing analysis) to identify persistent avionics issues (step 1004) to identify and remove anomalies from continuous avionics malfunctions, for example, the process may gauge the total duration during which each aircraft registered low IQI values. By normalizing this against the aircraft’s overall flight time and setting a threshold of 0.25 (indicating 25% problematic reporting time), aircraft surpassing this limit are labeled as problematic, and their associated anomalies are set-aside. The system may store information regarding problematic aircraft in a database to blacklist them permanently or for a certain period of time. This is to speed up the filtering process next time the aircraft enters the area of interest.

[0037] The process may also involve identifying and excluding aircraft that are expected (based on roll, pitch, yaw data over short periods of time) to be performing extreme flight maneuvers (step 1006) again before or after the threshold-crossing analysis. By analyzing the altitude profiles and identifying aircraft that exhibited multiple sharp ascents and descents of several hundred feet, this noise is eliminated as a source as well.

[0038] The process also involves identifying agglomerative clustering. An agglomerative clustering method is applied to the residual NIC anomalies. This algorithm spotlights IQI anomalies across aircraft with close temporal (step 1008) and spatial (step 1010) proximity. By setting a dual threshold of for example, 50 km spatially and 30 minutes temporally, isolated anomalies were further eliminated.

[0039] The remaining anomalies are classified as potential GPS interference events. In the study, the filtering removed approximately 97% of the primary anomalies, yielding 9330 anomalies that were potentially associated with interference, and the clustering algorithm further separated these anomalies into 1901 distinct jamming events. It is worth noting that the count of these events is sensitive to the clustering algorithm’s thresholds. In regions with dense air traffic, smaller thresholds enhance precision but may also separate anomalies belonging tothe same event into multiple events. Conversely, in sparser regions, wider thresholds prevent overlooking events due to infrequent air traffic.

[0040] The process then further analyzes the potential GPS interference events deeper to gauge their validity by accessing supplement aviation information (step 1012). By further inspecting the NIC anomalies and air traffic within the detected interference regions, associations are identified among multiple aircraft. In particular, data is identified that is associated with multiple aircraft displaying a NIC value drop concurrently or nearly concurrently, and recovering either upon exiting the affected area or collectively once the interference ceased. This pattern strongly indicates temporary interference in these regions, with the degree of certainty increasing as more aircraft are affected. Additionally, if NIC values of different aircraft change concurrently or nearly concurrently, this is a strong indicator for interference and that a jamming device is being turned on or off. During the jamming event however, the NIC changes do not occur concurrently but occur when the different aircraft enter or leave the affected airspace. In certain cases, supplemental information may also be available to provide further context, such as the issuance of notices posted by aviation authorities (NOTAMs), and results of the process may be used to provide notices posted by aviation authorities.

[0041] Once interference has been detected and characterized, the location of the jamming region is determined. Further, in accordance with certain aspects, pinpointing the origin of the interference not only aids authorities in taking timely corrective measures but also provides valuable insights into the nature and intent of the disruptions. The approach disclosed herein may be tailored for specific challenges posed by large-scale events.

[0042] Figure 4A diagrammatically shows a portion of the curved surface of Earth 200 as well as aircraft 202, 204, 206, 208, 210. The system identifies locations of changes in NIC or NAC value with respect to the threshold. For example, aircraft 208 may be experiencing a sudden drop in NIC or NAC value below the threshold while aircraft 204 is experiencing a sudden increase in NIC value above the threshold. Systems and methods of the invention are well suited for situations in which the jamming signal is strong enough to interrupt GPS services up to the jammer’s radio horizon, so the negatively impacted area is only restricted by the jammer’s line of sight, which in turn is limited by the Earth’s curvature and terrain. Under conditions of relatively flat terrain, such a radio horizon generally spans around 400-500km considering aircraft at cruising altitudes of up to 45,000ft. In certain conditions however, the jammer’s radio horizon may be impacted by geological features or may be intentionally shaped to have directionality toward, for example, a neighboring region.

[0043] Whenever an aircraft flies into or out of this interference region, the NIC signal exhibits a drop or recovery in its value as it crosses this radio horizon. Since aircraft 208 is experiencing a sudden drop in NIC value below the threshold and aircraft 204 is experiencing a sudden increase in NIC value above the threshold, the jammer’s radio horizon may be determined as shown at 212 in Figure 4B. Each location at which an aircraft experiences a threshold-cross of the NIC or NAC value indicates that the radio horizon of the aircraft is at that moment partially contiguous with a portion of the radio horizon of the jamming device (which is unknown). Hence, each start or end of a NIC or NAC anomaly provides data points that trace the contour of the jammer’s radio horizon 212. The location of the jamming device is shown at 201 in Figure 4B but this is unknown prior to the analyses, which is why the aircraft radio horizons at the locations of threshold-crossing are determined.

[0044] Figure 5A diagrammatically shows a portion of the curved surface of Earth 200 as well as aircraft 222, 224, 226, 228, 230. Again, the system identifies locations of changes in IQI value with respect to the threshold. For example, aircraft 228 may be experiencing a sudden drop in IQI value below the threshold while aircraft 224 is experiencing a sudden increase in IQI value above the threshold. The jammer’s radio horizon may be shaped by natural or human- made obstructions that limit the radio horizon. For example, natural objections may include a mountain range, and human-made obstructions may purposefully shape the radio horizon only to a particular neighboring region. Again, systems and methods of the invention are well suited for situations in which the jamming signal is strong enough to interrupt GPS services up to the jammer’s radio horizon, so the negatively impacted area is only restricted by the jammer’s line of sight, which in turn is limited by the Earth’s curvature and terrain.

[0045] Whenever an aircraft flies into or out of this interference region as shown in Figure 5B, the NIC or NAC signal exhibits a drop or recovery in its value as it crosses this radio horizon. Since aircraft 228 is experiencing a sudden drop in NIC value below the threshold and aircraft 224 is experiencing a sudden increase in NIC value above the threshold, the jammer’s radio horizon may be determined as shown at 232 and 234 in Figure 5B. Hence, each start or end of a NIC or NAC anomaly provides data points that trace the contour of the jammer’s radio horizon 232, 234. Again, each location at which an aircraft experiences a threshold-cross of the NIC or NAC value indicates that the radio horizon of the aircraft is at that moment partially contiguous with a portion of the radio horizon of the jamming device (which is unknown). Again, the location of the jamming device 403 is unknown unknown prior to the analyses, which is why the aircraft radio horizons at the locations of threshold-crossing are determined.

[0046] With reference to Figure 3B, the system identifies locations of aircraft when IQI values (e.g., NIC values or NAC values) cross below the threshold and when IQI values cross above the threshold (step 1014) as discussed above. To infer information about the jammer’s whereabouts from this information, the system makes use of the fact that the radio horizon is reciprocal, meaning that the moment when the aircraft crosses the jammer’s radio horizon is also the moment when the jammer first appears on the aircraft’s horizon. Analogously, the moment the aircraft disappears behind the jammer’s radio horizon is the same moment when the jammer disappears from the aircraft’s line of sight. The system then identifies estimated jammer radio horizon information from the aircraft radio horizons at the threshold-crossing locations, (step 1016). This information alone is useful for identifying an estimated jammer interference region away from which aircraft may be directed (step 1018), and is useful for switching aircraft near the estimated jammer interference region to use non-GNSS-based instruments for approaches and departures (step 2020).

[0047] Figure 6 shows at 250 a top view of a region of interest that includes an airspace 252 that is adversely affected by a jamming device. As shown, aircraft 254 within the airspace 252 will report low IQI values, while aircraft 256 that are not within the affected airspace 252 will report high IQI values. It is also possible that aircraft (e.g., 258) outside of the affected airspace 252 may report low IQI values for reasons unrelated to the jamming. Figure 7 shows at 260 an illustrative side view of the jamming interference region with the Earth being shown flat, causing the jamming horizon 266 to be curved. As aircraft enter the interference region 260 crossing the jamming horizon as shown at 261, 263, the IQI values will fall and the system will record the location of the aircraft associated with the drop in IQI values. As aircraft leave the interference region 260 crossing the jamming horizon as shown at 262, 264, the IQI values will rise and the system will record the location of the aircraft associated with the rise in IQI values. Each time an aircraft enters or exits the interference region 260, its IQI value drops (enters) or rises (exits), providing associated locations of the jamming device’s interference regions. The location of the jamming device 268 may further be found by finding the position that lies on each of the aircraft radio horizons as crossed by the aircraft at their corresponding falls and rises of the NIC values.

[0048] The shape of the jamming interference region may or may not be symmetric. For example, the jammer’s transmission may be blocked in certain areas (e.g., by natural or human- made obstructions) and may be directionally propagated toward a neighboring region as discussed above with reference to Figures 5A and 5B. With each radio horizon observation at an aircraft’s respective moment of interference entry or exit, the system acquires informationabout the jammer’s location as it lies somewhere on each of these circles. Hence, obtaining multiple of these circles and calculating their intersections provides an estimate of the jammer’s position. With reference again to Figure 3B, the system may then identify the region with the highest overlap or density of intersections of the aircraft radio horizons. The system may then further use the highest region of overlap of the aircraft radio horizons to infer a location of the jamming device (step 1022).

[0049] Figure 8 shows radio horizons 270, 272, 274 generated based on location information associated with GNSS signal quality information that has crossed the threshold value either rising above the threshold or falling below the threshold. The intersection of the radio horizons is shown at 276, which indicates the location of the jammer. Using the steps discussed herein, the signal strength data and location data are captured, filtered, and analyzed to identify threshold-crossing data, which is further analyzed to determine the jammer’s horizon. The steps discussed herein are performed by a processing system (of for example, one or more computer processors), such as the processing system 280 that includes a data collection module 282, a filtering module 284, a threshold-crossing module 286, a horizontal identification module 288, and a location analysis module 290 in accordance with an aspect of the present invention as shown in Figure 9.

[0050] Further, the system may develop a simulation of the extended jammer’s radio horizon based on the estimated jammer interference region, the estimated jamming device location, a terrain / elevation data set, and any additional available information (step 1024). The estimated jammer device location may be provided, for example, to update national security databases and to trigger international alert actions (step 1026). By such actions discussed herein, GNSS- based aviation systems are significantly improved in reliability and utility as aircraft may be directed around or switched to non GNSS-based navigation systems based on the estimated jammer interference region and / or the estimated jamming device location. The information could also be used to warn pilots that aim to fly through a GNSS-denied airspace so they know what to expect and be prepared. This information could also be used by the Air Navigation Service Provider (e.g. the FAA) as input for their surveillance system so that the system knows it cannot rely on ADS-B (which relies on GPS and does not work without it) anymore and has to switch to a different means of surveillance such as RADAR. Alternatively, the information could be used to highlight affected airspaces in the air traffic controller or aviation engineer’s surveillance display for better situational awareness. A further application of the information about the presence of GNSS interference is that Air Navigation Service Providers could use itto switch to alternative air traffic surveillance systems other than ADS-B since ADS-B relies on GPS and hence becomes unavailable when GPS is unavailable.

[0051] In particular, the system may use special data structures that maximize efficiency in determining spatial proximity (e.g., binary trees of k-dimensional points (KD Trees)).Otherwise computational complexity would be too high to do this in real-time if there is a lot of air traffic in the region of interest. The clustering algorithm may use an additional threshold on the number of different aircraft in the cluster. So, if there are only 2 aircraft in the cluster, it does not raise an alarm because it could just be coincidence and not interference. But if there are 5 or more aircraft having issues, the confidence about the interference increases and monitoring is triggered. The algorithm may also detect the end (or a change) of an interference event either through a timeout (haven ’t seen any anomaly in a while so the interference probably ended) or through one or more aircraft flying through the interference region without any IQI degradations. The system also stores a significant amount of information along with the IQI threshold crossings, including globally unique aircraft identifiers that allow identification of the aircraft across systems, the lowest IQI value (to get an indication of how strong the degradation was), start and end of the degradation along with the 3D aircraft position at these times (if available). In addition, the system may automatically produce maps and diagrams such as the ones shown in Fig 1 and Fig 2, and generate reports and data bundles with all relevant information in a structured format to provide full situational awareness to the systems and people using the information. Further. The determined jamming device location may be confirmed or refined by correlating it with additional supplement information such as satellite images, databases of military infrastructure.

[0052] With reference to Figure 9, the data recordation module 282 of the air traffic surveillance and management system 280 records avionics data 283 from aircraft in a region of interest, and the avionics data includes signal quality information and aircraft location information as discussed above. The signal quality information (IQI data) may for example, in the form of NAC values or NIC values that are transmitted from each aircraft, and the aircraft location information may be GNSS data. The threshold-crossing and filtering module 284 receives the avionics data 283 and both applies the threshold-crossing analysis discussed above and filters the data (in either order) to provide a filtered set of threshold-crossing data 285. The clustering module 286 identifies clusters of the data 285 that are temporally or spatially proximate to identifying IQI anomalies with close temporal or spatial proximity to remove isolated IQI anomalies as discussed above. The clustering module 286 may further optionally access support aviation authority information as also discussed above.

[0053] The filtered and clustered threshold-crossing data 287 provided by the clustering module 286 is provided, in accordance with an aspect of the present invention, to the horizon identification module 288 that identifies aircraft radio horizons at the threshold-crossing instances, and estimates a jammer interference region 289, based on which the action module 290 may take any of numerous actions discussed herein. For example, the action module 290 may direct aircraft around the jammer interference region, may switch aircraft approaching or departing from an airport near the jammer interference region to non GNSS-based navigation, and / or may even analyze the aircraft radio horizons data to identify an estimated location of the jamming device as discussed herein. The system may also conduct a simulation using the determined jamming device location to predict the full extent of the jammer interference region without the necessity to have aircraft flying there first.

[0054] In particular, and with reference again to Figure 3B, once the system has identified the location of the jammer, the system may direct aircraft around the jammer’s interference region (step 1018). With reference to Figure 3C, the system may switch aircraft to use non-GPS- based instruments for approach and departure at airports near the jammer interference region (step 1020), and / or may enable updates of national security databases and trigger international alert actions (step 1026). Additionally, the system may switch from GNSS data input to alternative PNT information sources for other onboard systems such as controller pilot data link communications (CPDLC), ground proximity and terrain warning systems, collision avoidance systems, auto pilot systems and others. The system may also identify a high region of overlap of aircraft radio horizons to estimate a jamming device location (step 1024), and update national security databases and trigger international alert actions (step 1026).

[0055] Figure 10 shows at 300 a number of (circular) radio horizons during detected interference transitions, and Figure 11 shows a density map 302 of the intersections of the jammer horizons, showing that the largest concentration of horizon intersections is at location 304, the likely location of the jammer.

[0056] Figure 12 shows an air traffic surveillance and management system 400 that includes one or more computer processing and communication systems 402 (e.g., including the processing system 230) for executing the functionality detailed above, including communicating with aircraft on the ground 404, aircraft that are arriving 406, aircraft that are departing 408, and aircraft that are en route at upper elevations 410, 412. Once the jammer’s interference region and optionally the jammer’s estimated location are determined, the system may direct aircraft (e.g., 410, 412) around the identified jammer interference region or notify pilots of the existence of the interference in the region. The system may also direct aircraft(e.g., 406, 408) to use non-GNSS-based instruments for approach and departure at airports near the jammer location, as well as for general navigation through the region. The system may also enable updates of national security databases 414 and trigger international alert actions with respect to the identified location of the GNSS jammer. Through the use of these actions, air traffic surveillance and management may be significantly improved by increasing the safety of the air traffic management systems, by preventing diversions of flights, and by reducing delays of the air traffic.

[0057] Systems and methods of various aspects of the present invention enable continuous, real-time surveillance across extensive regions. Certain strengths of the detection methodology lie in its heightened sensitivity and cost-efficiency, offering significant advantages over traditional methods such as interference hunting. Surprisingly, the system has identified that the prevalence of GNSS RF interference is far more extensive than initial expectations.

[0058] Those skilled in the art will appreciate that numerous modifications and variations may be made to the above disclosed embodiments without departing from the spirit and scope of the present invention.

[0059] What is claimed is:

Claims

1. An air traffic surveillance and management system comprising: a data recordation module for recording avionics data from aircraft in a region of interest, the avionics data including signal quality information and aircraft location information; a threshold-crossing and filtering module for processing the avionics data to identify a filtered set of falling changes in signal quality information falling below a threshold signal quality value, and for processing the avionics data to identify a filtered set of rising changes in signal quality information rising above the threshold signal quality value, collectively filtered threshold-crossing data; a clustering module for identifying filtered threshold-crossing data associated with any of spatial or temporal clusters; and an action module that identifies a jamming interference region based on the thresholdcrossing data associated with any of spatial or temporal clusters.

2. The air traffic surveillance and management system of claim 1, wherein the GNSS signal quality information are NIC values.

3. The air traffic surveillance and management system of claim 1, wherein the GNSS signal quality information are NAC values.

4. The air traffic surveillance and management system of any of claims 1-3, wherein the threshold-crossing and filtering module identifies persistent avionics issues.

5. The air traffic surveillance and management system of any of claims 1-4, wherein the threshold-crossing and filtering module identifies extreme flight maneuvers.

6. The air traffic surveillance and management system of any of claims 1-5, wherein the clustering module identifies NAC or NIC anomalies across aircraft with close spatial proximity to each other.

7. The air traffic surveillance and management system of any of claims 1-6, wherein the clustering module identifies NAC or NIC anomalies across aircraft with close temporal proximity to each other.

8. The air traffic surveillance and management system of any of claims 1-7, wherein the action module further directs aircraft around the jamming interference region.

9. The air traffic surveillance and management system of any of claims 1-8, wherein the action module further directs aircraft approaching and departing near the jamming interference region to use non GNSS-based navigation equipment.

10. The air traffic surveillance and management system of any of claims 1-9, wherein the system further includes a horizon identification module that generates aircraft horizon data representative of aircraft radio horizons associated with the filtered threshold-crossing data.

11. The air traffic surveillance and management system of claim 10, wherein the action module determines a location of a jamming device based on spatial overlap of the aircraft horizon data.

12. A method of providing surveillance and management in an air traffic surveillance system, said method comprising: recording avionics data from aircraft in a region of interest, the avionics data including GNSS signal quality information and aircraft location information; processing the avionics data to identify falling changes in GNSS signal quality information falling below a threshold signal quality value and to identify rising changes in GNSS signal quality information rising above the threshold signal quality value, the falling changes in GNSS signal quality information falling below the threshold signal quality value and the rising changes in GNSS signal quality information rising above the threshold signal quality value being collectively referred to as threshold-crossing data; analyzing the threshold-crossing data from the threshold-crossing module to detect temporal or spatial clusters of threshold-crossing data; and identifying a jamming interference region based on the clusters of threshold-crossing data.

13. The method of claim 12, wherein the GNSS signal quality information are NIC values.

14. The method of claim 12, wherein the GNSS signal quality information are NAC values.

15. The method of any of claims 12-14, wherein the method further includes filtering identified persistent avionics issues.

16. The method of any of claims 12-15, wherein the method further includes filtering identified extreme flight maneuvers.

17. The method of any of claims 12-16, wherein the analyzing the threshold-crossing data further includes identifying NAC or NIC anomalies across aircraft with close temporal proximity to each other.

18. The method of any of claims 12-17, wherein the analyzing the threshold-crossing data further includes identifying NAC or NIC anomalies across aircraft with close spatial proximity to each other.

19. The method of any of claims 12-18, wherein the method further includes directing aircraft around the jamming interference region.

20. The method of any of claims 12-19, wherein the method further includes directing aircraft approaching and departing near the jamming interference region to use non GNSS- based navigation equipment.

21. The method of any of claims 12-20, wherein the determining the method further includes generating aircraft horizon data representative of aircraft radio horizons associated with threshold-crossing data.

22. The method of claim 21 , wherein the method further includes determining a jamming location based on the aircraft radio horizon data.

23. An air traffic surveillance and management system comprising: a data recordation module for recording avionics data from aircraft in a region of interest, the avionics data including GNSS signal quality information and aircraft location information; a threshold-crossing and filtering module for processing the avionics data to identify a filtered set of falling changes in GNSS signal quality information falling below a threshold signal quality value, and for processing the avionics data to identify a filtered set of rising changes in GNSS signal quality information rising above the threshold signal quality value, collectively filtered threshold-crossing data; a horizon identification module that generates aircraft horizon data representative of aircraft radio horizons associated with the filtered threshold-crossing data; and an action module that identifies a location of a jamming device based on spatial overlap of the aircraft horizon data.

24. The air traffic surveillance and management system of claim 23, wherein the system further includes a clustering module for identifying filtered threshold-crossing data associated with any of spatial or temporal clusters.

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