Global Navigation Satellite System Interference Research Articles

A deep learning GNSS spoofing and jamming detection method with dual-frequency C/N0 heatmap

Abstract The Global Navigation Satellite System (GNSS) is vulnerable to interference due to the open signal structure and low signal strength, posing a significant threat to the billions of terminals worldwide that rely on GNSS receivers for precise Positioning, Navigation, and Timing (PNT) services. In this paper, we propose a cloud-edge framwork for GNSS spoofing and jamming monitoring, comprising the data acquisition module, GNSS monitoring module, detecting and reporting module. In this framwork, we design a Deep Learning (DL) method for detecting GNSS interference through Dual-frequency Carrier-to-Noise density ratio (C/N0) heatmaps (DD-C/N0). This method involves extracting and correlating features from C/N0 heatmaps of visible navigation satellites operating in the GPS L1 and L2 frequency bands, allowing the identification of anomalous patterns. A U-BLOX receiver was utilized to capture the GNSS satellite signals, while commercial jammers and Software-Defined Radio (SDR) HackRF One kits were employed to simulate the interference sources. Experimental results demonstrate that the proposed method achieves significantly higher performance, with an accuracy of 99% and 98% on the public dataset and real-time testing data, compared to unsupervised, semi-supervised, and supervised detectors that rely solely on single-channel data (L1 frequency band). Integrated with the DD-C/N0 method, the online GNSS monitoring system will be improved and deployed to automate spoofing and jamming detection tasks in the next step.

GNSS interference monitoring and detection based on the Swedish CORS network SWEPOS

Abstract The presence of Global Navigation Satellite System (GNSS) interference poses a significant risk to both GNSS and the infrastructures that depend on them. This article describes how an existing GNSS infrastructure, the Swedish Continuously Operating Reference Stations network SWEPOS with more than 500 stations, can be leveraged to control the quality of GNSS signals and to be able to detect interferences in GNSS and adjacent frequency bands. This study introduces a SWEPOS-based automatic system that effectively detects GNSS interference by examining the signal-to-noise ratio (SNR) of Global Positioning System, Globalnaya Navigatsionnaya Sputnikovaya Sistema, Galileo, and BeiDou frequency bands. By comparing the SNR of tracked satellites against historical data, the system detects radio frequency interference (RFI) by statistically characterizing the SNR of simultaneously tracked satellites. Furthermore, based on the SNR characteristics across satellites, the system distinguishes between RFI and non-RFI sources. The effectiveness of the detection system is demonstrated through both simulated signal interference waves and real-world interference events.

Geodetic advances in Estonia 2018–2022

Abstract Geodetic advances in Estonia within the Nordic Geodetic Commission activity term 2018–2022 are reviewed. The new Estonian height and gravity systems and a new geoid model, EST-GEOID207, have been implemented. A national global navigation satellite system (GNSS) satellite data centre (ESTPOS) was established at the Estonian Land Board (ELB). ELB has also acquired instruments capable of detecting GNSS interference. ELB and Latvian Geospatial Information Agency conducted the Interreg-funded joint Estonian–Latvian cross-border project “Harmonization of Estonian and Latvian geodetic systems in the border areas” within 2021–2022.

A Novel GNSS Sweep Interference Detection and Mitigation Method Based on Radon–Wigner Transform

In this paper, a novel Radon-Wigner transform (RWT) based sweep interference detection and mitigation method has been proposed for global navigation satellite system (GNSS) receivers working in the disturbing scenarios. The proposed GNSS sweep interference detection and mitigation method has been tested using real GPS L1-C/A data disturbed by the sweep interference in a controlled test bench experiment. The results have shown that the proposed method can effectively deal with the cross-term problems with the conventional time-frequency (TF) analysis techniques and provide much improved GNSS interference detection performance in comparison to the existed Wigner-Ville distribution (WVD) and Wigner-Hough transform (WHT) approaches; and the characteristic parameters of the GNSS sweep interfering signal can be efficiently estimated according to the peak position of the RWT computed in the parameter space. In order to excite the GNSS sweep interfering signal, the dechirping and notch filtering techniques have been suggested to realize the GNSS anti-interference functionality, providing satisfactory anti-interference performance for GNSS receivers, this has been further verified by the GNSS signal acquisition experiment in the interfering environment.

Comparative analysis of different empirical mode decomposition-kind algorithms on sea-level inversion by GNSS-MR

Abstract The rising sea level caused by global climate change might impact the human living environment. Global navigation satellite systems (GNSS)-multipath reflection (MR) technology holds significant potential for monitoring tide level changes. GNSS-MR technology typically employs low-order polynomials to extract the signal-to-noise ratio (SNR) residuals containing GNSS interference signals. It utilizes Lomb-Scargle (LSP) spectral analysis or empirical mode decomposition (EMD) to obtain the dominant frequency of the SNR residuals, which is then converted into tidal heights. However, as the satellite elevation angle increases, the GNSS interference signals decrease and the traditional method does not adapt well to the extraction of SNR residuals under such conditions. A series of improved EMD-kind algorithms, namely ensemble empirical mode decomposition (EEMD), complementary ensemble empirical mode decomposition (CEEMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEDMAN), have been proposed to address the shortcomings of EMD algorithms such as end effect and mode aliasing. However, these improved EMD-kind algorithms have yet to be reported in sea level inversion. This study investigates the mitigation effects of EMD-kind algorithms on GNSS-MR direct signal and noise to improve the stability and accuracy of an SNR residual sequence with high satellite elevation angles. Experimental data from the HKQT station for one week and the SC02 station for one year are utilized to validate the effectiveness and accuracy of these algorithms in extracting SNR residuals. Compared to the traditional polynomial method, the experimental results demonstrate that all EMD-kind algorithms effectively address the distortion issue in traditional inversion methods under long periods, higher satellite elevation angles, and low GNSS receiver sampling rates. Among these algorithms, the results from the experiments show that ICEEMDAN consistently provides the best inversion accuracy. The results of the comparative analysis show that ICEEMDAN effectively reduces non-interference signals in SNR residuals at higher satellite elevation angles, expanding the useable range of satellite elevation angles and improving the utilization and temporal resolution of GNSS data inversion. Hence, it is an effective and appropriate approach to improving the accuracy of GNSS-MR tide level monitoring.

Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System

Interference signals cause position errors and outages to global navigation satellite system (GNSS) receivers. However, to solve these problems, the interference source must be detected, classified, its purpose determined, and localized to eliminate it. Several interference monitoring solutions exist, but these are expensive, resulting in fewer nodes that may miss spatially sparse interference signals. This article introduces a low-cost commercial-off-the-shelf (COTS) GNSS interference monitoring, detection, and classification receiver. It employs machine learning (ML) on tailored signal pre-processing of the raw signal samples and GNSS measurements to facilitate a generalized, high-performance architecture that does not require human-in-the-loop (HIL) calibration. Therefore, the low-cost receivers with high performance can justify significantly more receivers being deployed, resulting in a significantly higher probability of intercept (POI). The architecture of the monitoring system is described in detail in this article, including an analysis of the energy consumption and optimization. Controlled interference scenarios demonstrate detection and classification capabilities exceeding conventional approaches. The ML results show that accurate and reliable detection and classification are possible with COTS hardware.

A survey of GNSS interference monitoring technologies

With the increasing economic and strategic significance of the global navigation satellite systems (GNSS), interference events also occur frequently. Interference monitoring technologies aim to monitor the interference that may affect the regular operation of the GNSS. Interference monitoring technologies can be divided into three parts: interference detection and recognition, interference source direction finding, and interference source location and tracking. Interference detection aims to determine whether interference exists. This paper introduces the classification of interference and the corresponding detection methods. The purpose of interference recognition is to recognize and classify interference. It is often combined with pattern recognition and machine learning algorithms. Interference source direction finding aims to estimate the direction of the interference signal. There are three kinds of methods: amplitude, phase, and spatial spectrum estimation. Interference source location aims to estimate the position of the interference signal. It is usually based on the received signal strength (RSS), time difference of arrival (TDOA), frequency difference of arrival (FDOA), angle of arrival (AOA) or direction of arrival (DOA). Interference source tracking aims to track moving interference sources, and it is generally based on Kalman filter theory. This paper summarizes the interference monitoring technologies and their latest progress. Finally, prospects for interference monitoring technologies are offered.

An optimal design of GNSS interference localisation wireless security network based on time-difference of arrivals for the Arlanda international airport

AbstractToday, most of the aircrafts are navigated by global navigation satellite systems (GNSSs). Landing is a dangerous phase of a flight especially when an airport runway is not clearly seen from the aircrafts. In such cases, GNSSs are useful for a safe landing under the circumstances that healthy signals, free of any interference, reach to GNSSs receiver antennas mounted on the aircrafts. This shows the importance of establishing GNSS interference localisation security networks around airports. Designing a good configuration for the points with GNSS antennas at for receiving interference signals is important for a successful localisation of the interference device. Here, the time-difference of the arrivals of an interference signal to such points or anchor nodes (ANs), are used as observables, and a security network with four ANs is optimally designed along the runways of the Arlanda airport to reduce the dilution of precision (DOP) of the network. Our study showed that by such an optimisation, the maximum DOP value can reduce by 50% meaning a significant increase in the probability of a successful interference device localisation.

GNSS Interference Type Recognition With Fingerprint Spectrum DNN Method

It is known that a global navigation satellite system (GNSS) receiver is vulnerable to interference signals, and the interference type recognition, i.e., classification, is helpful for the receiver to sense the spectrum, select an efficient mitigation solution, or identify an interferer. However, conventional interference recognition methods are subject to feature-based expert classification methodologies that are very sensitive to the choice of features and often lack flexibility to different tasks. Recently, some researchers rely on convolutional networks and transform the interference recognition to image classification. Although straightforward, this type of method suffers problems when classifying more complex interference patterns. In this article, a new GNSS interference classifier is proposed, which is composed of a devised interference fingerprint spectrum (FPS) and an especially designed deep convolutional neural network, named as FPS deep convolutional neural network (DNN). The design of the FPS makes different interference signals more discernible. The proposed deep convolutional neural network greatly improves recognition accuracies, especially at low interference powers. The tests show that the average accuracy of the proposed FPS-DNN can reach more than 95% for nine types of interferences at −110 dBm power. This result is significantly superior to the feature-based expert classifier and the conventional convolutional network classifier. It is also demonstrated that the proposed FPS-DNN has a better generalization performance even if the interference is out of the parameter space of the training dataset.

Security that Matters: General Knowledge and Correlations in the Context of Applied Satellite Navigation, Specific Interference-Events and the Use of the GNSS-Technology

Abstract Navigation through global navigation satellite systems (GNSS) has become an indispensable part of modern life with threats such as GNSS interference, making it necessary to uncover relevant psychological aspects in the context of the GNSS construct, diverse interference events, and the use of related technologies. A total of n = 122 subjects participated in an online survey, which included scales and specifically constructed items on GNSS usage, acceptance, dependence, and self-assessed sense of direction and relevance of basic psychological needs. In addition, frequently emphasized factors influencing acceptance and use of diverse technologies were recorded according to the Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al., 2003). Correlation analyses showed that the frequency of GNSS use was associated with both effort expectation of appropriate technologies, hedonistic motivation, habits of using GNSS-enabled devices, and specific aspects of mobility. In terms of reported GNSS dependency, negative correlations were found with self-assessed orientation ability. It was also possible to identify voluntariness in the use of related technologies, the age of the users, and the relevance of self-determination as essential variables in the context of GNSS use. The results underline the need for further investigation of psychological aspects and contribute to existing discussions in the context of various threat scenarios.

Impact and Protection of GNSS Interference: Resilient Positioning in Next Generation Satellite-Based Railway Train Control

Background: The Global Navigation Satellite System (GNSS) has great potentials in next-generation railway train control systems. Considering the fail-safe characteristics of train control, the threat from GNSS interference may result in an increasing likelihood of outages of train positioning or even safety risks to the railway system. Objective: The interference protection solutions are investigated and demonstrated for achieving the resilient train positioning using GNSS. Methods: This paper describes the main types of GNSS interference and investigates the impact on Location Determination Unit (LDU) in the GNSS-based train control system. Specific architectures and solutions for interference detection and protection in both the position domain and measurement domain are presented. Results: Interference injection simulations are performed with both the GNSS spoofing and jamming signals, which evaluate the effects of interferences and demonstrate the protection performance of the presented solutions under GNSS attack scenarios. Conclusion: The interference protection solutions within both the position domain and measurement domain are effective and significant to mitigate the effects from the GNSS interference, which enables resilient train positioning to achieve safe train operation.

A Novel GNSS Interference Detection Method Based on Smoothed Pseudo-Wigner-Hough Transform.

This paper develops novel Global Navigation Satellite System (GNSS) interference detection methods based on the Hough transform. These methods are realized by incorporating the Hough transform into three Time-Frequency distributions: Wigner–Ville distribution, pseudo -Wigner–Ville distribution and smoothed pseudo-Wigner–Ville distribution. This process results in the corresponding Wigner–Hough transform, pseudo-Wigner–Hough transform and smoothed pseudo-Wigner–Hough transform, which are used in GNSS interference detection to search for local Hough-transformed energy peak in a small limited area within the parameter space. The developed GNSS interference detection methods incorporate a novel concept of zero Hough-transformed energy distribution percentage to analyze the properties of energy concentration and cross-term suppression. The methods are tested with real GPS L1-C/A data collected in the presence of sweep interference. The test results show that the developed methods can deal with the cross-term problem with improved interference detection performance. In particular, the GNSS interference detection performance obtained with the smoothed pseudo-Wigner–Hough transform method is at least double that of the Wigner–Hough transform-based approach; the smoothed pseudo-Wigner–Hough transform-based GNSS interference detection method is improved at least 20% over the pseudo-Wigner–Hough transform-based technique in terms of the zero Hough-transformed energy percentage criteria. Therefore, the proposed smoothed pseudo-Wigner–Hough transform-based method is recommended in the interference detection for GNSS receivers, particularly in challenging electromagnetic environments.

A New GNSS Interference Detection Method Based on Rearranged Wavelet-Hough Transform.

Since radio frequency interference (RFI) seriously degrades the performance of a global navigation satellite system (GNSS) receiver, interference detection becomes very important for GNSS receivers. In this paper, a novel rearranged wavelet–Hough transform (RWHT) method is proposed in GNSS interference detection, which is obtained by the combination of rearranged wavelet transform and Hough transform (HT). The proposed RWHT method is tested for detecting sweep interference and continuous wave (CW) interference, the major types of GNSS interfering signals generated by a GNSS jammer in a controlled test bench experiment. The performance of the proposed RWHT method is compared with the conventional techniques such as Wigner–Ville distribution (WVD) and Wigner–Hough transform (WHT). The analysis results show that the proposed RWHT method reduces the influence of cross-item problem and improves the energy aggregation property in GNSS interference detection. When compared with the WHT approach, this proposed RWHT method presents about 90.3% and 30.8% performance improvement in the initial frequency and chirp rate estimation of the GNSS sweep interfering signal, respectively. These results can be further considered to be the proof of the validity and effectiveness of the developed GNSS interference detection method using RWHT.

Navigator Notes

Navigator Notes

An Emergency Positioning System Fusing GEO Satellite Doppler Observation and INS for SOTM

This article presents a newly invented emergency positioning system deployed on a satellite communication on-the-move (SOTM) platform, which can extend the SOTM communication function to the positioning function. Inherent to the SOTM system's high reliability, this emergency positioning system can effectively estimate the vehicle's position by fusing a geosynchronous orbit (GEO) satellite Doppler frequency observation and inertial navigation system (INS) of a SOTM-mounted moving vehicle when the global navigation satellite system (GNSS) is unavailable. It is a great challenge to use GEO Doppler frequency measurement with weak observability to correct the INS's cumulative error to estimate the position of the vehicle. In this article, the traditional inertial measurement unit (IMU) pre-integration model is improved to meet the needs of large-scale and high-precision navigation. In addition, we use graph optimization (GO) to estimate all states in the trajectory, which can effectively improve the observability of the error and reduce the nonlinear influence. We evaluated the system on a Ku-band SOTM platform, and the experimental results indicate that the system can achieve a convergent positioning accuracy of tens of meters. This method can provide emergency positioning in the case of GNSS interference.

GNSS interference mitigation: A measurement and position domain assessment

Modern Global Navigation Satellite System (GNSS) receivers have to withstand significant levels of interference in order to operate under harsh conditions, such as in the presence of jamming and of other Radio Frequency (RF) threats. A possibility is to implement pre-correlation interference mitigation techniques that operate directly on the samples provided by the receiver front-end. This paper provides an assessment of five interference mitigation techniques at the measurement and position level. The analysis focuses on the Adaptive Notch Filter (ANF) and on four Robust Interference Mitigation (RIM) techniques. Several data collections were performed in the presence of jamming, and the data collected were used for the analysis that shows that RIM techniques do not introduce biases at both the measurement and position level. While the ANF delays pseudorange measurements, the biases introduced are predominantly common to all the observations with a negligible impact on a Single Point Positioning (SPP) solution.

Collaborative Solutions for Interference Management in GNSS-Based Aircraft Navigation.

Nowadays, the Global Navigation Satellite Systems (GNSS) technology is not the primary means of navigation for civil aviation and Air Traffic Control, but its role is increasing. Consequently, the vulnerabilities of GNSSs to Radio Frequency Interference, including the dangerous intentional sources of interference (i.e., jamming and spoofing), raise concerns and special attention also in the aviation field. This panorama urges for figuring out effective solutions able to cope with GNSS interference and preserve safety of operations. In the frame of a Single European Sky Air traffic management Research (SESAR) Exploratory Research initiative, a novel, effective, and affordable concept of GNSS interference management for civil aviation has been developed. This new interference management concept is able to raise early warnings to the on-board navigation system about the detection of interfering signals and their classification, and then to estimate the Direction of Arrival (DoA) of the source of interference allowing the adoption of appropriate countermeasures against the individuated source. This paper describes the interference management concept and presents the on-field tests which allowed for assessing the reached level of performance and confirmed the applicability of this approach to the aviation applications.

Frequency tracking and mitigation method based on CPHD filter and adaptive multiple linear Kalman notch filter for multiple GNSS interference

In this paper, a frequency tracking method based on a cardinalized probability hypothesis density (CPHD) filter with cardinality compensation and fuzzy-c means (FCM) clustering is proposed to precisely estimate multiple interference frequencies in the received Global Navigation Satellite System (GNSS) signal. In addition, an adaptive multiple linear Kalman notch filter is applied in order to mitigate multiple GNSS interference signals by using tracking results from the proposed CPHD filter. The cardinality refers to the number of interference frequencies, and estimation accuracy on the cardinality has an effect on the tracking and mitigation performance of the filter. For that reason, the cardinality compensation process and FCM clustering are added in the CPHD filter. The proposed tracking and mitigation method is evaluated by theoretical analysis including simulations. It is confirmed that the proposed algorithm has better tracking and mitigation performance compared with the conventional algorithms.

Metode Deteksi ‘Potential Security Threats’ Pada ADS-B Data dengan Memonitor EM Emisi Radiasi pada Jaringan Ethernet

Precision, Navigation, and Timing (PNT) system based on Global Navigation Satellite System (GNSS) becomes significant in the air traffic management, especially in the use of Automatic Dependent Surveillance Broadcast system (ADS-B) for air traffic monitoring. Therefore the integrity of GNSS is significant to provide a reliable data necessary for ADS-B. GNSS Interference due to intentional or unintentional surrounding signal source may decrease the integrity of GNSS signal and therefore may result in the in-accurate position data of ADS-B message. ADS-B message itself is also vulnerable from potential security threats in their network. This paper proposed a methodology to detect potential security threats of ADS-B network system for both GNSS signal and ADS-B data by measuring and monitoring the electromagnetic radiated emission from ethernet cable IPv4 Cat5.

GNSS interference reduction method for CORS site planning

Precision, Navigation, and Timing (PNT) system based on Global Navigation Satellite System (GNSS) becomes significant in the air, land, and sea traffic management. Integrity of GNSS is significant to provide a reliable real time PNT system such as CORS (Continuously Operating Reference Stations). GNSS Interference due to intentional or unintentional surrounding signal source may decrease the integrity of GNSS signal. Monitoring and identification of potential GNSS interference sources in the surrounding environment of CORS is significant. This paper proposed a methodology to reduce potential GNSS interference in a planned CORS site by first simulating the radiation pattern of potential source of interference to GNSS signal in the planned CORS sites. Thereafter ambient noise levels in the location of CORS may be measured to provide a reference point for analyzing the other potential sources of interferences. Based on these results, optimal location of CORS is chosen with the lowest possible unintentional interference signal from their surrounding. Measurement has been conducted in the location of CORS owned by BIG (Indonesian Agency for Geospatial Information), which is located in the rooftop of a building neara telecommunication tower.This method is necessary for CORS site planning to reduce potential GNSS interference sources in the environment of alternative sites.