Search Results (95)

Global Navigation Satellite System (GNSS) receivers remain highly vulnerable to intentional interference such as jamming and spoofing, necessitating robust mitigation strategies. This study presents a field-based experimental evaluation of interference suppression approaches in Controlled Reception Pattern Antenna (CRPA) systems, focusing on the comparative performance of conventional time-frequency domain techniques (adaptive notch filtering and pulse blanking) and advanced space-time adaptive processing (STAP). Two representative CRPA receivers were tested in vehicle-mounted experiments under sequential baseline, jamming, and spoofing conditions, with controlled interference generated using a HackRF One platform integrated with the GNSS-SDR. The performance assessment was based on logged GNSS, jammer, and RSSI data collected during 15 min vehicle-mounted dynamic trials, each consisting of 5 min baseline, 5 min jamming, and 5 min spoofing phases. While both approaches exhibited comparable performance under nominal conditions, significant differences emerged under spoofing. The time-frequency domain approach experienced severe degradation, including up to 90% satellite loss and HDOP values exceeding 100, whereas the STAP-based system maintained more than 95% satellite visibility and stable positioning with HDOP values below 1. These results indicate that the tested STAP-based CRPA configuration provided higher system-level stability than the time-frequency domain configuration under the evaluated interference conditions. The findings highlight the critical role of spatial–temporal processing in improving GNSS resilience and offer practical insights for the design of next-generation anti-jamming and anti-spoofing. Full article

In this work, we explore resilient cooperative localisation for electric vehicles subject to the hybrid attack of gradual global navigation satellite system (GNSS) drag-off spoofing along with received signal strength indicator (RSSI) jamming. In order to mitigate such attacks, a deep learning-based physical-layer security approach is presented. The presented approach includes a long short-term memory (LSTM) detector for attack detection, a regression-based RSSI signal purifier, and a cooperative fusion scheme, which decreases the dependence on the GNSS branch in case of attack detection. The proposed approach is validated via the Berlin Vehicle-to-Everything (V2X) dataset with respect to six scenarios, including benign GNSS-only and cooperative localisation, attacked localisation without defence, and attacked localisation with physical-layer security support. According to the experimental evaluation results, the considered hybrid attack significantly impacts the localisation accuracy, leading to an increase in the GNSS-only localisation error to root mean square error (RMSE) = 149.93 m, mean absolute error (MAE) = 129.81 m, and maximum error = 259.62 m. At the same time, the proposed cooperative localisation with physical-layer security decreases the attacked cooperative localisation error to RMSE = 4.00 m, MAE = 3.51 m, and maximum error = 12.01 m. Full article

The demand for reliable Positioning, Navigation, and Timing (PNT) solutions is rapidly increasing due to the growing need for precision, efficiency, and safety in unmanned systems. As operations become more autonomous, the reliance on accurate and continuous PNT data becomes critical for maintaining system integrity. The Global Navigation Satellite System (GNSS), while serving as the primary global PNT service, is vulnerable to interference, jamming, and spoofing attacks. This raises serious concerns, particularly for safety-critical applications, and urgently requires resilient Alternative PNT (A-PNT) solutions. An existing worldwide infrastructure, the Distance Measuring Equipment (DME) system, is considered one of the most promising candidates for A-PNT to address GNSS vulnerabilities. Utilising the carrier phase of the DME signal enables distance measurements with centimetre-level accuracy. However, due to the pulse system nature of DME transmissions and the sparsity of phase observations, conventional carrier tracking loops such as PLLs and FLLs struggle to maintain a reliable phase lock. To address these challenges, this work proposes a zero-crossing-integrated Kalman filter-based approach to track the DME carrier signal at an irregular rate. The performance of the proposed algorithm is validated through a series of drone tests at Cranfield University, UK. The validation results demonstrate that the proposed enhanced carrier tracking approach consistently delivers stable and accurate performance. Full article

In order to enable an autonomous navigation capability in environments where global navigation satellite systems (GNSSs) are either denied (e.g., areas with intentional jamming or spoofing) or not available yet (Moon, Mars), Sodern is currently developing star trackers for Earth-based aircrafts and space rovers. This system is designed to compensate for inertial sensor (IMU)-induced drifts by providing an absolute attitude reference. The resulting celestial navigation system (CNS) aims at providing a position evaluation with a 100 m class precision, independent of the mission duration. In this paper, we present the star tracker design with a specific focus on daytime capabilities and the hybridization strategy to implement the retrieved celestial attitude in the CNS. Additionally, we present two application cases currently under development at Sodern, for space rovers and aircrafts. We evaluate the typical performances that can be reached depending on the IMU and star tracker class in harsh environments (luminance, dynamics, radiations…). We conclude with a brief presentation of future developments in this field. Full article

GPS GNSS position signal manipulation in shipping can lead to significant navigational challenges. Such disruptions may result from various factors, including atmospheric conditions, satellite malfunctions, or intentional positioning satellite signal disturbance. Impacts on shipping operations include delays, increased operational costs, and safety risks for crews and vessels. Understanding these disturbances and their implications is crucial for enhancing maritime safety and efficiency. Common causes of GNSS disturbances in shipping include atmospheric effects such as ionospheric and tropospheric delays, satellite signal obstructions due to terrain or buildings, satellite malfunctions or failures, and intentional interference like jamming. These factors can lead to inaccuracies in positioning, affecting navigation and safety. GPS signals are vulnerable to various cyber threats, including spoofing, jamming, and signal interference. Spoofing involves sending counterfeit GPS signals to mislead receivers, while jamming disrupts the legitimate signals. Ensuring the integrity and security of GPSs is crucial for applications like navigation, timing, and critical infrastructure. Advanced encryption and authentication methods can help safeguard the security of GPS signals. These vulnerabilities can have profound implications for navigation systems and critical infrastructure. Enhancing GPS security requires a combination of advanced technologies and policies to improve signal integrity and authentication processes. The Global Positioning System (GPS) is the most widely used GNSS positioning method in commercial shipping. Moreover, deliberate disturbance technical birth mechanisms are similar across the field of GNSS systems. Therefore, this study focuses on the deliberate disturbance of the GPS, recognising the ability to upscale the research results to other commonly used GNSSs such as Beidou, Galileo, and Glonass. This paper introduces a behavioural approach to enhancing cybersecurity and preparedness to external threats in commercial shipping through European collaboration in the CyberSEA project. Full article

The increasing interest in the development and integration of navigation and positioning services across a wide range of receivers has exposed them to various security threats, including GNSS jamming and spoofing attacks. Early detection of jamming and spoofing interference is crucial to mitigating these threats and preventing service degradation. This research introduces an interference detection technique leveraging an AI algorithm applied to GNSS data utilizing various methods to enhance detection accuracy and efficiency. The objective was to use modern sensors and AI to develop an effective tool that detects, characterizes, and localizes interference, thereby reducing associated risks. These sensors and algorithms enable continuous GNSS interference monitoring and support real-time Decision-making. A server plays a crucial role in managing the entire system. Its primary function is to process data collected from various sensors referred to as nodes (e.g., static, rover, drone, and space) and from (public) GNSS networks as well as to perform localization using rotating-antenna nodes. Within the interference detection module, various methods were implemented at different points in the software receiver architecture. Each method’s certainty in identifying an interference source depends on its design and capabilities, with outcomes—whether positive or negative—being subject to potential accuracy or errors. To enhance the Decision-making process, an AI-based Decision-making block has been introduced to determine the presence of interference at a given epoch. The proposed interference monitoring methods were evaluated through experiments using GNSS signals under clean, jamming, and spoofing scenarios. The results demonstrate the techniques’ applicability across diverse scenarios, achieving high performance in interference detection, characterization, and localization. Full article

Safe train positioning is a key technology to make rail transportation more efficient and cost-effective. Within the EGNSS MATE project, the project partners SBB, DLR, and IABG researched the use of European Global Satellite Navigation Systems for this application. The main contributions are the development of a novel map-based sensor fusion algorithm, the development of a test catalogue for jamming and spoofing cyberthreats, and the collection of a large and rich dataset for testing and validation. The dataset includes over 200 h of sensor data and ground truth data, covering most of the Swiss normal gauge network. In addition, tests were conducted to assess the impact of jamming and spoofing attacks. Results show promising performance of the algorithms on most of the lines, excluding some long tunnels and sections with heavy multipath. The findings of the project results will help to introduce safe train positioning into ETCS by boosting development and standardisation efforts. Full article

Named entity recognition (NER), a core task in natural language processing (NLP), remains constrained by heavy reliance on annotated data, limited cross domain generalization, and difficulty in recognizing name entities out of vocabulary entities. In specialized domains such as analysis of Global Navigation Satellite System (GNSS) countermeasures, including anti-jamming and anti-spoofing, where datasets are small and domain knowledge is scarce, existing models exhibit marked performance degradation. To address these challenges, we propose HybridNER, a framework that integrates locally trained span-based models with large language models (LLMs). The approach employs a span prediction metasystem that first fuses outputs from multiple base learners by computing span to label compatibility scores and assigns an uncertainty estimate to each candidate entity. Entities with uncertainty above a preset threshold are then routed to an LLM for a second stage classification, and the final decision integrates both sources to realize complementary strengths. Experiments on multiple general purpose and domain specific datasets show that HybridNER achieves higher precision, recall, and F1 than traditional ensemble methods such as majority voting and weighted voting, with especially pronounced gains in specialized domains, thereby improving the robustness and generalization of NER. Full article

Compact multi-channel airborne radar stations increasingly rely on an LED-based visible light communication (VLC) service link under radio-frequency spectrum restrictions and strict end-to-end delay constraints. Despite the directional nature of optical links, the VLC channel remains vulnerable to active optical interference and signal injection; furthermore, when an AI-enabled integrity monitor is embedded into the receiver, the AI decision layer becomes a direct target of evasion and online poisoning. This paper proposes a lightweight, interpretable AI-based attack detection architecture in which a Poisson photon-counting observation model is used to form physically consistent features over the preamble and control-sequence interval, while the final decision is produced by an AI ensemble combining a monotonic logistic detector and a one-class detector. The considered threat profile includes sustained illumination and synchronized flashes (jamming/blinding), spoofing via false preambles, replay of recorded fragments, and online data poisoning during self-calibration. The adequacy of solutions is assessed using the detection probability P_D (ensemble: P_D ≥ 0.90 for DC-jamming mean-count increment Δλ_DC ≈ 7.56, pulsed-interference mean-count increment Δλ_pulse ≈ 12.89, and spoofing signal-scaling factor α ≈ 1.02), the false-alarm probability P_FA = 0.045, and the per-packet end-to-end latency (bounded by the observation-window duration LΔT = 20 μs, where window length L = 20 and interval duration ΔT = 1 μs), which confirms real-time CPU operation without GPU acceleration. Full article

As satellite navigation systems show vulnerabilities in specific circumstances such as urban canyons or jamming and spoofing situations, additional sensors such as cameras may be incorporated on the platform. Despite advancements in the robotics and computer vision community, which have led to increasingly accurate Simultaneous Localization and Mapping (SLAM) positioning solutions, visual navigation has its own vulnerabilities. It therefore remains of critical importance for many applications to study the integrity of fused navigation algorithms and their components, which is done less for SLAM than for satellite navigation. In this paper, a framework for integrity monitoring (IM) of a visual SLAM algorithm is proposed. A sensor-level IM scheme analyses feature reprojection errors. It is demonstrated that, in dynamic environments, multiple hypotheses can be generated from different subsets of extracted features. Additionally, the factor graph-based framework employs a fusion-level IM scheme which deals with these multiple hypotheses and selects the most probable one by calculating the sum of weighted measurement residuals. These concepts are applied to scenarios from real and simulated experiments in order to demonstrate applicability. Full article

Jammertest, held annually in Andøya, Northern Norway, is the world’s largest open test for evaluating the resilience of Global Navigation Satellite System (GNSS) technologies against jamming, meaconing, and spoofing threats. Set in a remote Arctic location ideal for high-power interference testing with minimal societal impact, the event brings together a wide range of participants, from academia and industry to government agencies, to conduct real-world GNSS interference testing from a comprehensive and up-to-date Test Catalogue. Organised by a coalition of Norwegian authorities, Jammertest offers a unique environment and an inclusive approach to foster advancements in GNSS resilience without relying on strict regulation. This paper describes the background, approach, and technical setup, such as the transmissions, for the test week. Full article

Global Navigation Satellite Systems are vulnerable to jamming and spoofing threats, compromising several critical applications. Existing detection methods based on hardware solutions (antenna array, spectrogram) are low-latency and accurate but require expensive hardware, while machine learning solutions are the most effective but require extensive training and lack adaptability. This work proposes an edge-based, statistical threat detector using crowdsourced GNSS data and fuzzy logic to integrate multiple anomaly indicators. A key feature is a C-/N₀-based crowdsourcing metric. Experiments show detection precision up to 88% for jamming and 97% for spoofing, with false positive rates around 1–2% and an average detection time of 10 s. Full article

Optical networks constitute the backbone of contemporary communication infrastructures, supporting massive bandwidth, low-latency services, and high levels of scalability across core, metro, and access domains. As these systems evolve toward elastic, software-defined, and multi-domain architectures, their exposure to sophisticated security threats increases significantly. This paper provides a comprehensive survey of vulnerabilities and countermeasures in modern optical networks, spanning the physical, control, and cross-layer dimensions. We analyze major architectures—including WDM, TDM, PON, EON, and IP-over-WDM—and examine how their structural properties shape their security posture. A threat taxonomy is presented covering physical-layer attacks such as fiber tapping, optical jamming, crosstalk exploitation, and signal injection; control-plane risks including spoofing, malicious signaling, and SDN manipulation; and broader cross-layer attack vectors. We review state-of-the-art defense mechanisms, including physical-layer security (PLS), spectrum randomization, chaotic optical coding, device-level authentication, survivability techniques, intelligent monitoring, and quantum-secure solutions such as QKD. By integrating insights from recent experimental and operational studies, the survey highlights emerging challenges and identifies open problems related to secure orchestration, multi-tenant environments, and quantum-era resilience. The objective is to guide researchers, engineers, and network operators toward robust and future-proof security strategies for next-generation optical infrastructures. Full article

The exploitation of Optical Inter-Satellite Links (OISLs) has the potential to provide significant benefits to GNSSs, offering clock synchronization via highly accurate time transfer, precise ranging, robustness against jamming and spoofing, high data rates, and freedom from signal frequency regulations. As with any new technology, it is crucial to conduct in-space experiments to demonstrate the capabilities of OISLs before widespread adoption. In this work, we present preliminary analyses of an in-orbit demonstrator concept, which is being designed under the name Optical Synchronized Time And Ranging (OpSTAR). It involves two satellites in a trailing configuration, each equipped with two laser terminals. On the ground, two co-located Optical Ground Stations (OGSs) are operated. Whenever both satellites are simultaneously visible from the OGSs, an OISL and two additional Optical Satellite-to-Ground Links (OSGLs) are established, forming a closed “measurement loop” between the two satellites and the two ground stations. We present a functional model for OISL and OSGL pseudorange observables. A cross-link clock observable is formed by differencing two one-way pseudoranges, from which a relative clock offset estimate is obtained. First, we analyze how modeling errors on differential delays in Two-Way Time Transfer (TWTT)—relativistic effects, atmospheric delays, hardware delays, and satellite dynamics during the exchange—impact the estimation accuracy. Next, we study the impact of individual error contributions to the overall zero-sum chain of clock offset estimates across the closed-loop. Results show that errors due to mis-modeling of relativistic effects, satellite dynamics and clock instability are negligible, while hardware and atmospheric delays require accurate calibrations to achieve TWTT at picosecond-level accuracy. Full article

The reliance on global navigation satellite systems (GNSS) for modern vessel poses a critical point of failure. GNSS is vulnerable to jamming, spoofing, and other threats that can increase the risk of accidents. In response, alternative sources of navigational information are being sought. R-Mode offers a promising solution by leveraging terrestrial infrastructure to provide PNT data independently of GNSS. A minimum of three stations in view is needed to obtain a position and timing information. While a single R-Mode station in view cannot provide independent positioning, the received data can still be used to validate a GNSS solutions and detect threats like spoofing or outages. In this study, we introduce a novel approach to validate GNSS positions using R-Mode ranging information from a single station by combining the expected accuracy of the measurements with the geometrical relationship between the GNSS solution and the known R-Mode transmitter location. Our method was tested with real measurements in post-processing, where simulated spoofing events were introduced to mimic real-world scenarios. During these events, the GNSS solution deviated by approximately 100 m from original position. Our technique successfully detected the spoofing instances and raised warnings to increase the awareness of GNSS-based navigation threats. Full article