Global Navigation Satellite System Jamming Research Articles

Recent Advances on Jamming and Spoofing Detection in GNSS

Increased interest in the development and integration of navigation and positioning services into a wide range of receivers makes them susceptible to a variety of security attacks such as Global Navigation Satellite Systems (GNSS) jamming and spoofing attacks. The availability of low-cost devices including software-defined radios (SDRs) provides a wide accessibility of affordable platforms that can be used to perform these attacks. Early detection of jamming and spoofing interferences is essential for mitigation and avoidance of service degradation. For these reasons, the development of efficient detection methods has become an important research topic and a number of effective methods has been reported in the literature. This survey offers the reader a comprehensive and systematic review of methods for detection of GNSS jamming and spoofing interferences. The categorization and classification of selected methods according to specific parameters and features is provided with a focus on recent advances in the field. Although many different detection methods have been reported, significant research efforts toward developing new and more efficient methods remain ongoing. These efforts are driven by the rapid development and increased number of attacks that pose high-security risks. The presented review of GNSS jamming and spoofing detection methods may be used for the selection of the most appropriate solution for specific purposes and constraints and also to provide a reference for future research.

GNSS jamming detection using attention-based mutual information feature selection

Global navigation satellite systems (GNSS) are extensively utilized for military and civilian applications. Unfortunately, because of the signal weakness, GNSS is susceptible to interference, fading, and jamming, which reduces the position accuracy. Therefore, it would be beneficial to have a simple and highly accurate model for detecting the jamming signals to improve the GNSS receiver accuracy. In this paper, we propose a hybrid deep learning (DL) model for predicting jamming signals. Initially, we utilize a feature selection algorithm that combines mutual information (MI) with the minimal redundancy maximum relevance (mRMR) technique to identify the most crucial features. Subsequently, the model undergoes training using a soft attention-double-layer bidirectional long short-term memory (A-DBiLSTM) model. This particular model has shown outstanding performance in comparison to other DL models when applied to datasets collected from both kinematic and static jamming scenarios. To assess the effectiveness and efficiency of the proposed MI feature selection algorithm, we evaluate its performance through the calculation of confusion matrices and conducting numerical simulations. The simulation results of the A-DBiLSTM model demonstrate higher accuracy, precision, recall, and F1Score\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathrm {F1_{Score}}$$\\end{document} of 98.82%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98.82\\%$$\\end{document}, 98.4%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98.4\\%$$\\end{document}, 98.68%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98.68\\%$$\\end{document}, and 98.36%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98.36\\%$$\\end{document}, respectively. By employing the MI feature selection algorithm, dimensionality reduction is achieved. Moreover, the MI feature selection algorithm reduces 19%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$19\\%$$\\end{document} of the learning time with almost the same accuracy.

Evolution of Timing Services From 5G-A Toward 6G

5G-Advanced and 6G networks will serve as critical infrastructure for society and will enable a new generation of immersive use cases, such as the metaverse, wherever people roam. Absolute time is an essential component to ensure critical use cases, synchronize media playout, and timestamp events to be used in machine learned contexts. Until now, timing on the go has mainly been acquired by satellite, e.g., Global Navigation Satellite System (GNSS), but a terrestrial timing solution using cellular networks is required to extend coverage to deep indoors and to offer resilient operation in GNSS-denied environments (e.g., due to GNSS signal interference, jamming and spoofing). In this paper, we detail the use cases for timing to be provisioned by 5G-Advanced and 6G networks. Then, we discuss the architectural enablers for timing as a service in current 5G and 5G-Advanced standard and timing resiliency enablers under discussion in the 3rd Generation Partnership Project (3GPP). Finally, we discuss the gaps and research challenges to be solved in 6G for future-proof timing solutions.

SPWVD-PSD 겹침 이미지를 이용한 전이학습기반 GNSS 재밍식별기법

In this paper, a transfer learning-based global navigation satellite system (GNSS) jamming classification scheme using overlapped images of the smoothed pseudo-Wigner-Ville distribution (SPWVD) and power spectral density (PSD) is proposed. Five types of jamming are considered: amplitude modulation jamming (continuous wave interference), linear chirp jamming, frequency modulation jamming, narrow-band noise jamming, and distance measurement equipment jamming (pulse jamming). The performance of the proposed scheme is shown to be the F1 score and the confusion matrix for pre-mentioned 5 jamming types with no jamming case. A representative conventional transfer learning-based GNSS jamming classification scheme uses a concatenated image comprising a spectrogram, in-phase and quadrature constellation, PSD, and histogram. However, in conventional schemes, the spectrogram

Positioning of Quadruped Robot Based on Tightly Coupled LiDAR Vision Inertial Odometer

Quadruped robots, an important class of unmanned aerial vehicles, have broad potential for applications in education, service, industry, military, and other fields. Their independent positioning plays a key role for completing assigned tasks in a complex environment. However, positioning based on global navigation satellite systems (GNSS) may result in GNSS jamming and quadruped robots not operating properly in environments sheltered by buildings. In this paper, a tightly coupled LiDAR vision inertial odometer (LVIO) is proposed to address the positioning inaccuracy of quadruped robots, which have poor mileage information obtained though legs and feet structures only. With this optimization method, the point cloud data obtained by 3D LiDAR, the image feature information obtained by binocular vision, and the IMU inertial data are combined to improve the precise indoor and outdoor positioning of a quadruped robot. This method reduces the errors caused by the uniform motion model in laser odometer as well as the image blur caused by rapid movements of the robot, which can lead to error-matching in a dynamic scene; at the same time, it alleviates the impact of drift on inertial measurements. Finally, the quadruped robot in the laboratory is used to build a physical platform for verification. The experimental results show that the designed LVIO effectively realizes the positioning of four groups of robots with high precision and strong robustness, both indoors or outdoors, which verify the feasibility and effectiveness of the proposed method.

Assessment of Android Network Positioning as an Alternative Source of Navigation for Drone Operations

Applications of drones have increased significantly in the past decade for both indoor and outdoor operations. In order to assist autonomous drone navigation, there are numerous sensors installed onboard the vehicles. These include Global Navigation Satellite Systems (GNSS) chipsets, inertial sensors, barometer, lidar, radar and vision sensors. The two sensors used most often by drone autopilot controllers for absolute positioning are the GNSS chipsets and barometer. Although, for most outdoor operations, these sensors provide accurate and reliable position information, their accuracy, availability, and integrity deteriorate for indoor applications and in the presence of radio frequency interference (RFI), such as GNSS spoofing and jamming. Therefore, it is possible to derive network-based locations from Wi-Fi and cellular transmission. Although there have been many theoretical studies on network positioning, limited resources are available for the expected quantitative performance of these positioning methodologies. In this paper, the authors investigate both the horizontal and vertical accuracy of the Android network location engines under rural, suburban, and urban environments. The paper determines the horizontal location accuracy to be approximately 1637 m, 38 m, and 32 m in terms of 68% circular error probable (CEP) for rural, suburban, and urban environments, respectively, and the vertical accuracy to be 1.2 m and 4.6 m in terms of 68% CEP for suburban and urban environments, respectively. In addition, the availability and latency of the location engines are explored. Furthermore, the paper assesses the accuracy of the Android network location accuracy indicator for various drone operation environments. The assessed accuracies of the network locations provide a deeper insight into their potential for drone navigation.

Detecting GNSS Jamming and Spoofing on Android Devices

<h3>Abstract</h3> Global navigation satellite system (GNSS) location engines on Android devices provide location and navigation utility to billions of people worldwide. However, these location engines currently have very limited protection from threats to their position, navigation, and time (PNT) solutions. External sources of radio frequency interference (RFI) can render PNT information unusable. Even worse, false signals or spoofing can provide a false PNT solution to Android devices. To mitigate this, four detection methods were developed and evaluated using native location parameters within Android: Comparing the GNSS and network locations, checking the Android mock location flag, comparing the GNSS and Android system times, and observing the automatic gain control (AGC) and carrier-to-noise density (C/N₀) signal metrics. These methods provide a powerful means to significantly increase the robustness of the Android GNSS-based PNT solution and are implemented in the GNSSAlarm Android application to demonstrate real-time jamming and spoofing detection.

Ground Speed Optical Estimator for Miniature UAV.

In a conventional Unmanned aerial vehicles (UAV) navigational system Global Navigation Satellite System (GNSS) sensor is often a main source of data for trajectory generation. Even video tracking based systems need some GNSS data for proper work. The goal of this study is to develop an optics-based system to estimate the ground speed of the UAV in the case of the GNSS failure, jamming, or unavailability. The proposed approach uses a camera mounted on the fuselage belly of the UAV. We can obtain the ground speed of the airplane by using the digital cropping, the stabilization of the real time image, and template matching algorithms. By combining the ground speed vector components with measurements of airspeed and altitude, the wind velocity and drift are computed. The obtained data were used to improve efficiency of the video-tracking based on a navigational system. An algorithm allows this computation to be performed in real time on board of a UAV. The algorithm was tested in Software-in-the-loop and implemented on the UAV hardware. Its effectiveness has been demonstrated through the experimental test results. The presented work could be useful for upgrading the existing MUAV products (with embedded cameras) already delivered to the customers only by updating their software. It is especially significant in the case when any necessary hardware upgrades would be economically unjustified or even impossible to be carried out.

Low‐complexity framework for GNSS jamming and spoofing detection on moving platforms

Performance of global navigation satellite systems (GNSSs) mounted on aerial platforms could be degraded by the presence of jamming or spoofing threats. Detection of jamming and spoofing is essential considering practical applications of satellite navigation in passenger aircrafts, unmanned aerial vehicles (UAVs), helicopters and fighters. Different algorithms and methods have been proposed for detection of these threats; however, their usage has many limitations because of their demanding weight, size and computational complexity, when embedded on aerial systems. In this study, the authors develop a theoretical framework to detect the presence of the threat of UAVs. The idea is based on the fact that, due to the UAV motion, the samples of received signal power from a fixed threat and from a GNSS satellite have different empirical probability density functions. Moreover, by using two antennas (an omnidirectional and a down-tilted-directional), they introduce a new method to distinguish between aerial and ground-based threats. The proposed algorithms have a low-computational burden and can consider the fading loss as well. Simulation results show the superior performance of the proposed methods, in terms of detection and false alarm probability, compared to the existing methods.

GNSS jamming detection of UAV ground control station using random matrix theory

Global navigation satellite systems (GNSS) are the main navigation and control systems in unmanned aerial vehicles (UAVs) and their ground control stations. Without the GNSS signals, the UAV and its ground control stations cannot follow the waypoints of the desired path in jamming environments. In this paper, two new methods for detection of GNSS signal jamming attack for UAV ground control station are proposed based on random matrix theory. By using limiting distribution of mean vector and asymptotic behavior of the defined test statistic, a hypothesis test is introduced and evaluated to detect presence of jamming signal. Simulation results show that the proposed methods have significant performance in terms of detection and false alarm probabilities. Compared to existing methods, at low jamming-to-signal ratio (JSR), more than 2.5 dB improvement is achieved.

GNSS Vulnerabilities and Existing Solutions: A Review of the Literature

This literature review paper focuses on existing vulnerabilities associated with global navigation satellite systems (GNSSs). With respect to the civilian/non encrypted GNSSs, they are employed for proving positioning, navigation and timing (PNT) solutions across a wide range of industries. Some of these include electric power grids, stock exchange systems, cellular communications, agriculture, unmanned aerial systems and intelligent transportation systems. In this survey paper, physical degradations, existing threats and solutions adopted in academia and industry are presented. In regards to GNSS threats, jamming and spoofing attacks as well as detection techniques adopted in the literature are surveyed and summarized. Also discussed are multipath propagation in GNSS and non line-of-sight (NLoS) detection techniques. The review also identifies and discusses open research areas and techniques which can be investigated for the purpose of enhancing the robustness of GNSS.

Dynamic Evaluation of GNSS Spoofing and Jamming Efficacy Based on Game Theory

The evaluation of the effectiveness of global navigation satellite system (GNSS) spoofing and jamming equipment is not only an important means of enhancing the power of modern satellite navigation countermeasures, but also a dynamic decision-making problem, whose complexity manifests as the uncertainty of information used to make decisions (e.g., fuzziness, randomness, and dynamics). Previously, full consideration was given to the dynamics of decision information, as well the fuzzy, stochastic problems of quantitative and qualitative data when integrated with subjective and objective information. In this study, first, a spoofing mode index was established based on the performance analysis results of the spoofing equipment and target receivers during antagonized navigation. Second, a combined interval number eigenvalue method (IEM) algorithm with ternary association numbers, and entropy-weighting of the interval were used to combine weights, while considering the uncertainty of judged index weights and their subjectivity. Using a fuzzy comprehensive assessment of the interval and the superposition of multiple expert-derived joint scores, a profit matrix was constructed and solved using game theory to reveal the advantages of this approach in dealing with dynamic problems. Finally, the uncertainty of decision information was fully considered by applying the proposed method to practical application and dynamic analysis.

A Two-Stage Interference Suppression Scheme Based on Antenna Array for GNSS Jamming and Spoofing.

Jamming and spoofing are the two main types of intentional interference for global navigation satellite system (GNSS) receivers. Due to the entirely different signal characteristics they have, a few techniques can deal with them simultaneously. This paper proposes a two-stage interference suppression scheme based on antenna arrays, which can detect and mitigate jamming and spoofing before the despreading of GNSS receivers. First, a subspace projection was adopted to eliminate the high-power jamming signals. The output signal is still a multi-dimensional vector so that the spatial processing technique can be used in the next stage. Then, the cyclostationarity of GNSS signals were fully excavated to reduce or even remove the noise component in the spatial correlation matrix. Thus, the signal subspace, including information of the power and the directions-of-arrival (DOAs) of the GNSS signals, can be obtained. Next, a novel cyclic correlation eigenvalue test (CCET) algorithm was proposed to detect the presence of a spoofing attack, and the cyclic music signal classification (Cyclic MUSIC) algorithm was employed to estimate the DOAs of all the navigation signals. Finally, this study employed a subspace projection again to eliminate the spoofing signals and provide a higher gain for authentic satellite signals through beamforming. All the operations were performed on the raw digital baseband signal so that they did not introduce additional computational complexity to the GNSS receiver. The simulation results show that the proposed scheme not only suppresses jamming and spoofing effectively but also maximizes the power of the authentic signals. Nonetheless, the estimated DOA of spoofing signals may be helpful for the interference source positioning in some applications.

Keeping UAVs Under Control During GPS Jamming

Unmanned aerial vehicles (UAV) are being more and more widely deployed. Although, initially most UAVs were controlled by a pilot, autonomy has increased. This requires the flight controller in the UAV to accurately know the current system state. Knowing the current position is of crucial importance. Fortunately, this can be reliably obtained from a global navigation satellite system (GNSS) receiver. Recently, it was shown that criminal GNSS jamming frequently occurs. First, this paper analyzes the risk posed by off-the-shelf civilian GPS jammers to UAVs. Then, as the main contribution of this paper, a countermeasure is described and implemented using custom hardware and software that allows the control radio to be used as a backup navigation source. Finally, the proposed solution is tested in ideal and field conditions. It is shown that good performance can be obtained by the presented system, almost without additional system cost.

GNSS Jamming Resilience for Close to Shore Navigation in the Northern Sea

Navigational error accounts for half of the accidents and serious incidents in close to shore maritime transport in Norway predominantly due to the rapidly changing weather conditions and the dangerous nature of the narrow inshore waters found along the Norwegian coast. This creates a dependence on Differential Global Positioning System (DGPS) use and any disruption to this service can lead to an increased accident rate. The aim of this paper is to research the jamming vulnerability of existing maritime receivers and to understand if an upgrade to a multi-constellation or multi-frequency receiver would improve system resilience. The novelty of this work is a comparison of jamming resilience between different combinations of multiple constellations (GPS and Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS)) and multi-frequency Global Navigation Satellite System (GNSS) signals. This paper presents results from GNSS jamming trials conducted in the northern part of Norway, confirming previous research and indicating that typical maritime GPS receivers are easy to jam and may produce erroneous positional information. Results demonstrate that the single frequency multi-constellation receivers offer better jamming resilience than multi-frequency (L1 + L2) GPS receivers. Further, the GLONASS constellation demonstrated a better resilience than GPS. Results demonstrate a known correlation between GPS L1 and L2 frequencies, as well as a probable over-dependence on GPS for signal acquisition, meaning that no signal can be received without GPS L1 present. With these limitations in mind, the authors suggest that the most economic update to the single frequency GPS receivers, currently used for maritime applications, should be multi-constellation GPS + GLONASS receivers. This solution is cheaper and it also offer better jamming resistance for close to shore navigation than dual frequency receivers.

Protecting GNSS Receivers From Jamming and Interference

Critical government and industry sectors (such as law enforcement, transportation, communication, and finance) are growing increasingly dependent on Global Navigation Satellite Systems (GNSS) for positioning, navigation, and timing. At the same time, the availability of low-cost GNSS jamming devices are presenting a serious threat to GNSS and increasing the likelihood of outages to infrastructures relying on GNSS. The attacks range from malicious parties intentionally jamming GNSS signals within a targeted geographical region to uninformed users causing accidental interference. This paper is an overview of different approaches adopted to date to mitigate GNSS disruption caused by intentional and unintentional jamming. The first approach outlined in this paper is the use of inertial systems to aid GNSS. The second and third approaches are the filtering of jamming/interference in the spatial and time-frequency domains, respectively. The fourth approach is vector tracking of GNSS signals in the receiver.

고고도 장기체공 무인기와 의사위성/트랜시버를 활용한 국지적 대체항법에 관한 연구

Global navigation satellite systems (GNSS) is operating widely in civil and military area. GNSS signals, however, can be easily interfered because its signal is vulnerable to jamming. Thus, a sort of backup or alternative system is needed in order that the navigation performance is assured to a certain degree in case of GNSS jamming. In order to suggest a series of backup or alternative system of regional navigation, in this paper, we introduced a high altitude long endurance unmanned aerial vehicle (HALE UAV) with pseudolites using inverted GPS and transceiver system. We simulated the positioning error of the regional navigation system using HALE UAV with inverted GPS or transceivers concepts. We estimated the position error of HALE UAV calculate user position errors based on the position error of HALE UAV and general pseudorange error.

A Basic Study on the Jamming Mechanisms and Characteristics against GPS/GNSS Based on Navigation Warfare

It has been recognized that the risk from the vulnerability of GPS can lead to the extreme damage in the infrastructure of the civil and military in recent years. As an example, the intentional interference to GPS signal, named GPS jamming, was really performed to misguide GPS guided weapons during Iraq war in 2003, and the fact has also followed by the serious issues on GPS in civilian community. In the modernized military society, the navigation warfare(NAVWAR) based on the GPS jamming has been emerged and introduced as a military operation. The intentional interference to the future global navigation satellite system(GNSS) involving GPS must be also an important issue to civilian users in near future. This study is focused on the fundamental research prior to the research on "Potential principle of NAVWAR" under NAVWAR of the future warfare. In this paper, we would study on the investigation of NAVWAR based on electronic warfare(EW) and analyze characteristics of the jamming against GNSS's receivers. Then the general mechanism on GNSS jamming is proposed.