Search Results (250)

In recent years, the use of UAVs has become widespread. Long distance flight of UAVs requires obtaining precise geographic coordinates. Global Navigation Satellite Systems (GNSS) are the most common positioning models, but their signals are susceptible to interference from obstacles and complex electromagnetic environments. In this case, vision-based technology can serve as an alternative solution to ensure the self-positioning capability of UAVs. Therefore, a scene adaptive UAV visual localization framework (SAVL) is proposed. In the proposed framework, UAV images are mapped to satellite images with geographic coordinates through pixel-level matching to locate UAVs. Firstly, to tackle the challenge of inaccurate localization resulting from sparse terrain features, this work proposes a novel feature extraction network grounded in a general visual model, leveraging the robust zero-shot generalization capability of the pre-trained model and extracting sparse features from UAV and satellite imagery. Secondly, in order to overcome the problem of weak generalization ability in unknown scenarios, a descriptor incremental mapping module was designed, which reduces multi-source image differences at the semantic level through UAV satellite image descriptor mapping and constructs a confidence-based incremental strategy to dynamically adapt to the scene. Finally, due to the lack of annotated public datasets, a scene-rich UAV dataset (RealUAV) was constructed to study UAV visual localization in real-world environments. In order to evaluate the localization performance of the proposed framework, several related methods were compared and analyzed in detail. The results on the dataset indicate that the proposed method achieves excellent positioning accuracy, with an average error of only 8.71 m. Full article

Global navigation satellite system (GNSS) has been widely used in many fields due to their low cost and high positioning accuracy. Because of the open frequency of navigation signals, the low power of navigation signals, and the growing reliance of many modern wireless systems on satellite-based navigation, GNSS performance may be easily affected by interference signals. Monitoring and troubleshooting of interference sources are important means to guarantee the normal use of satellite navigation applications and are an important part of GNSS operation in complex electromagnetic environments; however, traditional angle of arrival (AOA) algorithms cannot efficiently operate with coherent signals, so a decoherence-based orientation scheme is proposed to optimize the AOA algorithm. Furthermore, a joint AOA and time difference of arrival (TDOA) interference localization algorithm is proposed for problems such as the lack of accuracy in a single interference source localization algorithm. Numerical simulation results show that decoherence-based AOA localization can be well applied to various interference signals, and the accuracy of the joint AOA and TDOA interference localization algorithm is higher than that of single-method interference localization. In addition, the physical verification further verifies the usability and reliability of the GNSS interference source positioning algorithm proposed in this paper. Full article

Low Earth orbit (LEO) satellites offer a revolutionary potential for positioning, navigation, and timing (PNT) services due to their stronger signal power and rapid geometric changes compared to traditional global navigation satellite systems (GNSS). However, dedicated LEO navigation systems face high costs, so opportunity navigation based on LEO satellites is a potential solution. This paper presents an orthogonal frequency division multiplexing (OFDM)-based LEO navigation system and analyzes its navigation performance. We use 5G new radio (NR) as the satellite transmitting signal and introduce the NR signal components that can be used for navigation services. The LEO NR system and a novel zero-padding correlation (ZPC) are introduced. This ZPC receiver can eliminate cyclic prefix (CP) and inter-carrier interference, thereby improving tracking accuracy. The power spectral density (PSD) for the NR navigation signal is derived, followed by a comprehensive analysis of tracking accuracy under different NR configurations (bandwidth, spectral allocation, and signal components). An extended Kalman filter (EKF) is proposed to fuse pseudorange and pseudorange rate measurements for real-time positioning. The simulations demonstrate an 80% improvement in ranging precision (3.0–4.5 cm) and 88.3% enhancement in positioning accuracy (5.61 cm) compared to conventional receivers. The proposed ZPC receiver can achieve centimeter-level navigation accuracy. This work comprehensively analyzes the navigation performance of the LEO NR system and provides a reference for LEO PNT design. Full article

Traditional structural damage detection relies on multi-sensor arrays (e.g., total stations, accelerometers, and GNSS). However, these sensors have some inherent limitations such as high cost, limited accuracy, and environmental sensitivity. Advances in computer vision technology have driven the research on vision-based structural vibration analysis and damage identification. In this study, an optimized Lucas–Kanade optical flow algorithm is proposed, and it integrates feature point trajectory analysis with an adaptive thresholding mechanism, and improves the accuracy of the measurements through an innovative error vector filtering strategy. Comprehensive experimental validation demonstrates the performance of the algorithm in a variety of test scenarios. The method tracked MTS vibrations with 97% accuracy in a laboratory environment, and the robustness of the environment was confirmed by successful noise reduction using a dedicated noise-suppression algorithm under camera-induced interference conditions. UAV field tests show that it effectively compensates for UAV-induced motion artifacts and maintains over 90% measurement accuracy in both indoor and outdoor environments. Comparative analyses show that the proposed UAV-based method has significantly improved accuracy compared to the traditional optical flow method, providing a highly robust visual monitoring solution for structural durability assessment in complex environments. Full article

New positioning solutions are a key factor in the pursuit of autonomous driving. Global Navigation Satellite System (GNSS) is the most common method; however, traditional systems may have high position errors due to the signal’s path between the satellite and the receiver’s antenna. In this work, we present a GNSS adaptive antenna with beamforming capabilities that can apply spatial filtering to mitigate interferences and improve satellite connectivity, reducing the positioning error. The array, developed with off-the-shelf GNSS antennas, was used to demonstrate the improvement of the gain comparatively to the traditional GNSS antenna, while maintaining circular polarization in all directions. A digital beamforming solution was employed with the software-defined platform based on a Xilinx ZCU216. The full system performance was tested in an anechoic chamber, where good results were obtained in both single- and multibeam scenarios, with great agreement between the simulated and measured data. The results presented in this paper validate the proposed Field-Programmable Gate Array (FPGA)-based antenna array and beamforming development platform, paving the way for the seamless and rapid design and testing of numerous antenna array geometries with up to 16 channels and beamforming algorithms, including adaptive ones. This powerful and versatile tool will accelerate research on the performance improvement of GNSS reception. Full article

The combination of GNSS RTK and INS offers complementary advantages but faces significant challenges in urban canyons. Frequent cycle slips in carrier phase measurements and ambiguity resolution algorithms increase computational burden without improving positioning accuracy. Additionally, environmental interference introduces noise into observations, potentially leading to complete signal loss. To address these issues, this paper proposes a factor graph optimization (FGO) positioning algorithm incorporating predictive observation factors. First, a penalized spline (P-spline) is constructed to predict and smooth Doppler measurements. The predicted Doppler is then fused with the dynamics model predictions to enhance robustness. Using predictive Doppler, carrier phase and pseudorange observations are reconstructed, generating predictive constraint factors to improve positioning accuracy. Real-world tests conducted in urban canyons, including Shanghai, demonstrate that the proposed method maintains stable positioning performance even under short-term signal outages, effectively mitigating cumulative positioning errors caused by data loss. Compared to traditional methods that rely solely on available observations, the proposed algorithm improves northward and dynamic positioning accuracy by 35% and 29%, respectively, providing a highly robust navigation solution for complex urban environments. Full article

Nowadays, high precision and reliability of Global Navigation Satellite Systems are increasingly important in positioning applications. Machine learning is used to improve the performance of the GSHARP PPP algorithm by reducing the effect of multipath on GNSS measurements. The clustering analysis is conducted on the primary GNSS data points with the goal of discovering and analyzing patterns in the multipath interference. This study represents an early attempt to apply AI to the GSHARP PPP algorithm. Since Lightweight Machine Learning is used in this research, it is easier to integrate and might lay the groundwork for future integration of advanced deep learning methods. About 50 h of data collected from different environments (e.g., highways and urban areas) serves as the training data for these algorithms, which ensures their robustness and real-world applicability. The use of machine learning clustering inside the PPP algorithm serves as a way to improve its performance against multipath effects, as well as provide a platform for subsequent development of precision GNSS systems through AI technologies. Full article

The Global Navigation Satellite System (GNSS) has emerged as a critical spatiotemporal infrastructure for ensuring the integrity of remote sensing data links. However, traditional GNSS antenna arrays, typically configured with the antenna spacing of half a wavelength, are constrained by the spatial limitations of remote sensing platforms. This limitation results in a restricted number of interference-resistant antennas, posing a risk of failure in scenarios involving distributed multi-source interference. To address this challenge, this paper focuses on the multidimensional trade-off problem in the design of compact GNSS anti-interference arrays under finite spatial constraints. For the first time, we systematically reveal the intrinsic relationships and game-theoretic mechanisms among key parameters, including the number of antennas, antenna spacing, antenna size, null width, coupling effects, and receiver availability. First, we propose a novel null width analysis method based on the steering vector correlation coefficient (SVCC), elucidating the inverse regulatory mechanism between increasing the number of antennas and reducing antenna spacing on null width. Furthermore, we demonstrate that increasing antenna size enhances the signal-to-noise ratio (SNR) while also introducing trade-offs with mutual coupling losses, which degrade SNR after compensation. Building on these insights, we innovatively propose a multi-objective optimization framework based on the non-dominated sorting genetic algorithm-II (NSGA-II) model, integrating antenna electromagnetic characteristics and signal processing constraints. Through iterative generation of the Pareto front, this framework achieves a globally optimal solution that balances spatial efficiency and anti-interference performance. Experimental results show that, under a platform constraint of 1 wavelength × 1 wavelength, the optimal number of antennas ranges from 15 to 17, corresponding to receiver availability rates of 89%, 72%, and 55%, respectively. Full article

Since 2022, DLR’s Mobile Rocket Base (MORABA) has observed jamming of GNSS signals on sounding rockets launched from Esrange in northern Sweden and Andøya Space Center (ASC) in northern Norway. The jamming primarily affected the GPS L1, Galileo E1 and BeiDou B1C and B1I signals on the L1 frequency band and was noticeable through a pronounced reduction in the carrier-to-noise ratio of the received GNSS signals. Jamming was observed in northern Sweden at an altitude above 22 km and in northern Norway at an altitude above 36 km. Geometric considerations made it possible to roughly localize the source of the jamming signals from the points of the flight path marking the start and end of interference. Full article

This paper addresses the critical issue of unwanted interference in airborne GNSS receivers, crucial for navigational safety. Previous studies often simplified the problem, but this work offers a comprehensive approach, considering factors like Earth’s reflective properties, 3D calculations, and distinct radiation patterns. It introduces Spatial Interference Distribution Expression Heat-map and Operation Efficacy Plot graphs to visualize interference distribution along flight paths. The results highlight the significance of physical configuration and distance from interference sources on receiver performance. The algorithm developed can assess interference effects on GNSS receivers and aid in selecting optimal flight paths for minimal interference. This research enhances understanding and management of unintentional interference in airborne navigation systems. Full article

Interference signals can disrupt global navigation satellite system (GNSS) receivers and degrade or deny a position-velocity-time (PVT) solution. After detecting an interference signal, classifying its type can provide insight into its cause and help determine the necessary next steps to counteract it. In this paper, we present a method for interference detection and a resource-efficient model-aided on-the-edge machine learning (ML) model for interference signal classification running on low-cost commercial-off-the-shelf (COTS) hardware, enabling a highly cost-effective spectral monitoring solution. The choice of detection metrics is justified based on real-world spectral monitoring data from a German highway and the capability of the ML model to generalize across different environments is demonstrated in an outdoor field test. Overall, we present an operationally ready GNSS interference detection and classification system. Full article

With the rapid advancement of unmanned system technologies, their applications in transportation, scientific research, economy, resource exploration, and military fields have become increasingly widespread. The navigation system, as a fundamental component of unmanned systems, plays a crucial role in ensuring their stability and reliability. However, as technology evolves, interference targeting Global Navigation Satellite Systems (GNSSs) has escalated, posing significant challenges in the research of unmanned systems. Navigation interference not only disrupts the normal operation of unmanned systems but also emerges as a pivotal element in counter-unmanned system strategies. This paper provides a comprehensive review of the classification of GNSS navigation interference and its potential impacts, thoroughly analyzing and comparing the strengths and weaknesses of various anti-GNSS interference technologies. Finally, the paper offers insights into the future development trends of anti-interference technologies for unmanned systems, aiming to provide valuable references for future research. Full article

Global Navigation Satellite System (GNSS) reference receivers are an essential part of ground stations that make the operation of Satellite-Based Augmentation Systems (SBAS) possible. Recently, there has been increasing concern about spoofing and interference events, which may seriously hinder the operation of GNSS receivers in liability- and safety-critical applications and, in particular, SBAS ground stations. In this context, the goal of this paper is two-fold. On the one hand, a set of spoofing and interference detection techniques should be presented specifically tailored to operate with the outputs provided by a NovAtel G-III SBAS reference receiver. On the other hand, assessing these techniques with various tests conducted using a Safran Skydel GSG-8 GNSS RF simulator in order to validate their implementation and effectiveness is necessary. This work concludes with an analysis of the obtained results, providing insightful recommendations and guidelines. Full article

Global navigation satellite systems (GNSSs), with their ubiquitous coverage, have become a cornerstone of modern position, navigation, and timing (PNT) services. While their spread spectrum communication offers inherent, albeit partial, resilience against interference, GNSSs remain a prime target for malicious actors seeking to disrupt or degrade precise location and time synchronization. Jamming mitigation has been an active research area for over three decades. Despite diverse research efforts, a key weakness in the literature is the absence of rigorous, methodologically sound testing of proposed mitigation techniques in a controlled laboratory environment. This work addresses this deficiency by exploring the challenges of evaluating GNSS receiver performance and response under interference and by proposing a more robust methodological framework for result interpretation. We present a custom simulation environment that enables repeated, controlled measurements of GNSS receiver behavior under various jamming attacks, revealing discrepancies between expected performance and real-world observations. Using three low-cost receivers as a case study, we demonstrate the inherent uncertainty in the results, the unpredictable behavior of the receivers’ embedded software, and appropriate statistical analysis practices. A key contribution of this work is a publicly available dataset of extensive GNSS receiver response measurements acquired under controlled interference conditions using an advanced signal generation and a comprehensive satellite constellation simulator. Full article

Global Navigation Satellite Systems (GNSSs) are the primary source of information for Positioning, Navigation, and Timing (PNT) in the maritime sector; however, they are vulnerable to unintentional or deliberate interference, such as jamming, spoofing, or meaconing. The continuous monitoring of GNSS signals is crucial for vessels and mobile maritime platforms to ensure the integrity, availability, and accuracy of positioning and navigation services. This monitoring is essential for guaranteeing the safety and security of navigation and contributes to the accurate positioning of vessels and platforms involved in hydrographic and oceanographic research. This paper presents the implementation of a complex system for monitoring the quality of signals within the GNSS spectrum at the Maritime Hydrographic Directorate (MHD). The system provides real-time analysis of signal parameters from various GNSSs, enabling alerts in critical situations and generating statistics and reports. It comprises four permanent stations equipped with state-of-the-art GNSS receivers, which integrate a spectrum analyzer and store raw data for post-processing. The system also includes software for monitoring the GNSS spectrum, detecting interference events, and visualizing signal quality data. Implemented using a Docker-based platform to enable efficient management and distribution, the software architecture consists of a reverse proxy, message broker, front-end, authorization service, GNSS orchestrator, and GNSS monitoring module. This system enhances the quality of command, control, communications, and intelligence decisions for planning and execution. It has demonstrated a high success rate in detecting and localizing jamming and spoofing events, thereby improving maritime situational awareness and navigational safety. Future development could involve installing dedicated stations to locate interference sources. Full article