Search Results (4,678)

This study applied SBAS-InSAR to a dense Sentinel-1 Single Look Complex (SLC) archive (146 scenes) to monitor the 1915 Çanakkale Bridge between 2022 and 2024 (data up to 7 January 2025 were available and considered in the time-series reconstruction). The analysis produced LOS mean velocity maps and pointwise displacement time series, revealing localized displacement concentrated near the Lapseki approach. Extreme LOS values reached approximately −101 mm (min) and +77 mm (max) across the domain, while maximum cumulative LOS displacement near the Asian anchorage approached −90 mm. These satellite observations suggest that ground-related processes may contribute to the detected observed movement; however, LOS-only measurements and limited in situ validations preclude a definitive separation between structural and geotechnical drivers. We therefore recommend targeted GNSS/levelling campaigns, ascending (ASC)–descending (DSC) InSAR fusion, and formal uncertainty reporting to better constrain the deformation sources and magnitude. The study concluded that the SBAS-InSAR method is effective for long-term, contactless monitoring of bridges and similar mega structures. It was also determined that this method can be used to identify critical areas requiring ongoing monitoring. Full article

Global Navigation Satellite Systems (GNSS) are highly affected in equatorial regions, especially due to the formation of Equatorial Plasma Bubbles (EPBs), which cause disturbances in the ionosphere resulting in different forms of signal degradation. Despite Colombia’s privileged geographic position, its limited monitoring infrastructure hinders the detection and mitigation of these effects. This study proposes the development of a Low-Cost Scintillation Laboratory (LCSL) using a cognitive radio–based approach for real-time scintillation monitoring, aimed at improving GNSS reliability. The system was designed following a Systems Engineering methodology, defining functional architectures and constraints. A communication system model was developed to account for EPBs’ effects on GNSS signals, while cognitive radio algorithms within a Software-Defined Radio (SDR) framework enabled real-time detection, monitoring, and alert generation. To implement this approach, monitoring stations were deployed in Bogotá, Cartagena, and Santa Marta utilized low-cost GNSS receivers integrated with Machine Learning (ML) algorithms for the automatic classification of scintillation events. Additionally, the system’s accuracy was validated by comparing experimental data with historical records from the Geophysical Institute of Peru (IGP). The results demonstrated that the integration of cognitive radio and ML-based detection enhanced precision and adaptability compared to traditional methods. The network of monitoring stations effectively validated the system’s performance, providing valuable insights into equatorial ionospheric dynamics. This study contributes to the advancement of monitoring methodologies and highlights the importance of accessible infrastructure for mitigating EPB effects on GNSS, ultimately fostering more resilient navigation and communication systems. Full article

Dual-axis rotational inertial navigation systems (DRINSs) have been widely used in marine navigation due to their high accuracy. However, the long-term operation of a DRINS over weeks poses a significant challenge to its reliability. In order to address the fault diagnosis challenges faced by DRINSs on long-endurance vessels in global navigation satellite system (GNSS)-denied environments, this paper proposes a fault diagnosis algorithm for redundant DRINSs based on geometric constraint observation. The mechanization of dual DRINSs is implemented using a globally referenced framework. A residual-normalized strong tracking filter based on geometric constraint observation is employed to estimate the fault states of the dual DRINSs. A highly robust fault diagnosis method is proposed to detect and diagnose faults in the inertial devices of dual DRINSs. The experimental results show that the proposed algorithm exhibits excellent performance with a diagnostic accuracy of 98.67% and low diagnostic delay. Full article

This study evaluates the quality of ionosphere-corrected bending angle products from 12 satellite radio occultation (RO) missions, with data provided by the ROM SAF and CDAAC data centers. The missions include MetOp-B/C, Sentinel-6, Spire, COSMIC-2, KOMPSAT-5, and TerraSAR-X. The assessment focuses on bending angle quality control (QC), bias and noise characteristics at 65–80 km altitude, and statistical errors, with ERA5 data used as the reference. For quality control, Spire products achieved the highest pass rate, exceeding 99%. Products from the two MetOp satellites and Sentinel-6 exhibited pass rates above approximately 90%. The COSMIC-2 series had a pass rate of ~81%, while KOMPSAT-5 and TerraSAR-X had pass rates of 62% and 68%, respectively. Concerning bending angle biases, slightly larger biases were observed in MetOp setting events. Biases from other missions were mostly within the range of 0–0.05 μrad. Regarding noises, Sentinel-6 recorded the smallest bending angle noise (0.87 μrad), whereas TerraSAR-X (2.3 μrad) and KOMPSAT-5 (1.9 μrad) showed the largest noise magnitudes. Systematic differences in bending angles from all 12 RO missions were generally consistent below 60 km, while their standard deviations show good consistency below 35 km. In the middle stratosphere (35–50 km), MetOp-B/C and Sentinel-6 displayed the smallest standard deviations. Spire values are 1–2% larger, COSMIC-2 values 5–10% larger, and TerraSAR-X values the largest. Since ERA5 data also contain inherent uncertainties, particularly above 60 km, the findings of this study can only serve as a preliminary reference for users applying these datasets in weather and climate research. Future work will investigate the detailed causes of discrepancies among different datasets at high altitudes. Full article

The Mw 6.4 Petrinja earthquake on 29 December 2020 ruptured the Petrinja-Pokupsko fault system in central Croatia, producing widespread coseismic deformation and subsequent postseismic processes. This study examines ground displacements in the Petrinja area from 2019 to 2022 using Sentinel-1 SAR data processed with SBAS time series analysis. Interferometric phase residuals were filtered using temporal coherence masking and RMS cut-off criteria to ensure high-quality displacement estimates. Line-of-sight (LOS) velocity fields were derived separately for ascending and descending tracks, combined into horizontal and vertical components, and rotated into a fault-parallel direction. Fault-parallel velocities were also extracted with pixel-wise coseismic offsets removed to isolate postseismic transients. Pre-event displacements are generally small and often within measurement uncertainties. However, because the 2019–2022 observation window includes the mainshock and concentrated early postseismic motion, robust estimation of long-term interseismic rates (millimeters per year) is not possible from this dataset. Such rates from independent regional GNSS measurements are therefore included solely for tectonic context and visual illustration. A clear surface displacement jump exceeding 20 cm was detected, with opposite signs in ascending and descending geometries, reflecting predominant right-lateral strike-slip motion. Following the removal of the coseismic jump, weighted profile analysis identifies residual transients of up to ±1.5 cm/yr near the fault, consistent with dominant shallow afterslip. Possible contributions from viscoelastic relaxation are noted, as such processes produce broader, longer-timescale deformation patterns that cannot be excluded without extended observations or forward modeling. These geodetic observations quantify the immediate postseismic deformation and provide constraints on near-fault slip patterns following the mainshock. Full article

Accurate autonomous navigation of unmanned aerial vehicles (UAVs) in complex indoor environments where satellite signals are denied remains a critical challenge. Conventional state estimation methods, such as particle filters, often suffer from particle degeneracy and high computational costs, limiting their robustness and real-time applicability. Here, we introduce an adaptive particle filter-neural network (PF-NN) fusion framework that achieves high-fidelity cooperative localization for multi-UAV systems. Our approach integrates a lightweight neural network that optimizes particle weight allocation by learning from motion consistency, thereby mitigating sample impoverishment. This is coupled with an adaptive resampling strategy that dynamically adjusts the particle population based on the effective sample size, balancing computational load with estimation accuracy. By fusing ultra-wideband (UWB) inter-vehicle ranging with visual landmark observations, the system leverages both global and local constraints to achieve robust state estimation. In simulations involving six UAVs in a complex indoor setting, our algorithm demonstrated superior performance, achieving an average root-mean-square error (RMSE) of 0.437 m. This work provides a robust and efficient solution for multi-UAV cooperative localization, paving the way for reliable autonomous operations in GNSS-denied scenarios such as search-and-rescue and industrial inspection. Full article

Reliable positioning and mapping in roadway tunnels are crucial for vehicle-based monitoring and inspection, especially considering the challenging environmental conditions such as rapidly changing illumination, low-texture environments, and repetitive structural elements. While general LiDAR-inertial odometry (LIO) frameworks and loop-closure detection methods are effective in general scenarios, they often suffer from severe drift or incorrect loop constraints under these specific conditions. These challenges are further exacerbated by the inherent uncertainties associated with low-cost sensors. This paper introduces a narrow field-of-view LiDAR-centric RTK-visual-inertial SLAM system enhanced by three key modules: semantic-assisted loop detection and matching, two-stage RTK quality control, and adaptive factor graph optimization (FGO). In the first module, the proposed semantic loop descriptor (SLD) matching is used to determine the potential loop closure locations and then integrates the corresponding constraint as graph nodes. The quality control module addresses RTK outlier rejection during tunnel entry and exit, employing an event-driven stochastic model to characterize the uncertainty between RTK and the other sensors, effectively suppressing RTK-induced errors. FGO module performs optimization by incorporating LIO, RTK, and loop closure factors, employing a keyframe-based strategy to produce globally optimized poses while continuously updating the map. The proposed Tunnel-SLAM was evaluated against state-of-the-art SLAM algorithms in four extended roadway tunnels, ranging in traveling distance approximately from 5 to 10 km. Experimental results demonstrate that the proposed SLAM achieved a final drift of less than 2 m with loop closure, demonstrating significantly reducing the drift, while other existing SLAM frameworks fail catastrophically or have large drift. 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

Automotive radar has emerged as a pivotal technology for navigation in GNSS-denied environments, offering superior robustness to adverse weather and fluctuating lighting conditions compared to vision or LiDAR-based sensors. Despite these advantages, the inherent sparsity and noise of radar measurements often lead to degraded estimation accuracy and system reliability. To address these challenges, various radar-based localization frameworks have been explored, ranging from optimization-based and Extended Kalman Filter (EKF) approaches fused with Inertial Measurement Units (IMUs) to point cloud registration techniques like Iterative Closest Point (ICP). While filter-based methods are favored in multi-sensor fusion for their proven stability, ICP is widely utilized for high-precision pose estimation in point-cloud-centric systems. In this study, we propose a novel Radar-Inertial Odometry (RIO) framework that synergistically integrates ICP-based relative pose estimation with model-based sensor fusion. The proposed methodology leverages relative transformations derived from ICP alongside ego-velocity estimations obtained from radar Doppler measurements. To effectively incorporate relative ICP constraints, a stochastic cloning technique is implemented to augment previous states and their associated covariances, ensuring that the uncertainty of historical poses is explicitly accounted for. The performance of the proposed method is validated using public open-source datasets, demonstrating higher localization accuracy and more consistent performance compared to existing algorithms used for comparison. Full article

Autonomous mobile robots require robust localization in complex and dynamic environments, where single-sensor solutions often fail due to accumulated drift or signal degradation. LiDAR–inertial odometry provides accurate short-term motion estimation, but suffers from long-term error accumulation, whereas RTK-GNSS offers absolute positioning that becomes unreliable under occlusion or multipath effects. To solve the above problems, this paper proposes an adaptive multi-sensor fusion positioning framework that dynamically fuses LiDAR, IMU, and RTK-GNSS data based on the real-time quality evaluation of sensors. The system uses the front-end tightly coupled LiDAR–IMU iterative extension Kalman filter (IEKF) as the core estimator and combines loop detection with incremental factor graph optimization to suppress long-term drift. In addition, a degradation detection method based on the minimum eigenvalue of the Jacobian matrix is proposed to identify unreliable matching constraints in real time. In order to avoid abrupt changes in positioning results caused by fluctuations in sensor data quality, the system adopts a smooth fusion strategy based on covariance weighting. Experiments on the KITTI benchmark and self-collected datasets demonstrate that the proposed method significantly improves localization accuracy and robustness compared with pure LiDAR-based approaches, achieving stable centimeter-level performance while maintaining real-time capability on embedded platforms. Full article

Space weather monitoring depends critically on passive sensor systems that detect and measure natural solar and geospace emissions without transmitting radio frequency energy. These include riometers, solar radio monitors, interplanetary scintillation detectors, GNSS-based ionospheric sensors, and broadband solar spectrographs that enable the provision of critical data required to forecast geomagnetic storms, protect critical infrastructures, and support aviation services, satellite operations, and defense services. However, with the increasing proliferation of radiocommunication technologies such as 5G/6G networks, dense HF/VHF/UHF deployments, and large constellations of low-Earth-orbit (LEO) satellites, the interference threat to these exceptionally sensitive receivers has grown. Most of these operate near the thermal noise floor and thus require strict protection criteria to ensure continuity of data. This review and perspective article provides a cross-disciplinary synthesis of scientific requirements, documented RFI case studies, and ongoing regulatory developments related to spectrum protection for passive space weather sensors. It systematically integrates perspectives on physical, technical, and regulatory aspects that are typically addressed separately in the literature. The article reviews the operating principles of major sensor classes and analyzes documented RFI cases affecting GNSS, riometers, CALLISTO, BINGO, and systems impacted by LEO satellite emissions, drawing from existing reports and regulatory submissions. Building on this evidence base, the work comparatively evaluates regulatory methods under consideration for WRC-27 shows that hybrid approaches combining primary allocations in core observation bands with secondary status and coordination procedures in adjacent bands offer the most viable path forward. This synthesis contextualizes and analyzes how technical protection criteria can be integrated with existing and evolving regulatory instruments to inform spectrum governance. The study concludes that without coordinated international spectrum management incorporating explicit protection thresholds and registration procedures, the long-term viability of space weather monitoring infrastructure faces significant risk in an increasingly congested radio frequency environment. Full article

Positioning, Navigation and Timing (PNT) methods in GNSS-challenged environments require multi-sensor and cooperative approaches to mitigate the low or complete unavailability of GNSS measurements. Many methods also rely on map databases and the availability of sensors throughout the environment. Data like Signal of Opportunity (SoO) ranges, Inertial Measurement Units, and camera data are often used to ensure measurement redundancy. Given the recent advancements in Artificial Intelligence (AI) image segmentation, especially the Segment Anything Model (SAM) and Depth Anything (DA) model, there is an opportunity to treat AI as a modern SoO. SAM can quickly and efficiently recognise distinct objects in any image, while DA can create a pixel-based depth map from any image. A novel architecture for combining multi-sensor cooperative positioning and a position integrity method with SAM and DA is proposed. In this paper, the initial feasibility study of using SAM and DA to determine the ranges from images is carried out. SAM and DA are tested on photographs taken in Vienna, Austria. The feasibility of establishing a functional relation between determined depth and ground truth distances is studied and demonstrated. Full article

Accurate modeling of tropospheric effects on GNSS signals is essential for achieving high-precision positioning, as the troposphere can delay pseudorange signals by up to 30 m in Standard Point Positioning applications. While empirical models, such as the Saastamoinen model, are commonly used to simulate tropospheric delay by separating it into the hydrostatic (ZHD) and wet (ZWD) components, these models often lack the realism needed to model the highly variable ZWD accurately. To address this limitation, Safran Electronics & Defense has developed an advanced machine learning-based model to enhance the realism of the unpredicted ZWD simulation within the Skydel-powered GNSS simulators. The model incorporates a feedforward neural network with two hidden layers, integrated with empirical methods for ZHD computation, resulting in a robust hybrid framework. The model is trained on a comprehensive 20-year dataset (2004–2024) collected from 221 GNSS stations worldwide and further refined using meteorological data from Open Meteo to ensure accurate input parameters. This innovative hybrid approach significantly enhances the realism of tropospheric delay modeling for Safran’s Skydel GNSS simulation software (version 24.4). Performance evaluations show a significant reduction in simulation errors across all tested stations, especially under complex and dynamic weather conditions. The paper details the new model’s design, training, and optimization processes, emphasizing the seamless integration of machine learning techniques within the Skydel simulator architecture. By delivering more realistic simulations, this methodology enhances the fidelity of GNSS signal modeling and establishes a new benchmark for the integration of machine learning into reliable GNSS simulators. Full article

In GNSS-denied large-scale outdoor environments, UAVs and UGVs that rely solely on visual–inertial odometry (VIO) suffer from accumulated global drift as the trajectory grows. Meanwhile, inter-platform ultra-wideband (UWB) ranging exhibits unknown, time-varying noise under NLOS/multipath, rendering naïve weighting unreliable. This paper presents an anchor-free collaborative localization framework for UAV–UGV teams that fuses pairwise UWB ranges (including UAV–UAV, UAV–UGV, and UGV–UGV) with onboard VIO in a factor-graph backend via a two-stage robust scheme. First, we bound VIO drift using per-agent state covariance and reject UWB outliers with a Mahalanobis gate, preventing early-stage bias when VIO is still accurate. Then, during global optimization, we adaptively estimate the Fisher information of UWB factors from measurement–state residuals, enabling online self-tuning of measurement confidence under time-varying SNR. Real-world experiments with three UAVs and two UGVs over multi-level rooftops and forest–open areas (~1.6 km²) show that, compared to an outlier-only variant, the proposed method further reduces localization RMSE by about 24.6% and maximum error by about 31.2% for both UAVs and UGVs, maintaining strong performance during long trajectories dominated by VIO drift and NLOS ranges. The approach requires no fixed anchors or GNSS and is applicable to UAV–UGV teams for disaster response, cooperative mapping/inspection, and bandwidth-limited operations. Full article

This paper investigates Direction-of-Arrival (DOA) estimation of Long-Range Navigation-C (Loran-C) signals using an Ultra-Short Baseline (USBL) receiving array. Two least-squares angle estimation approaches based on inter-element delay measurements are examined, including Correlation-based Least-Squares (Corr-LS) and a Zero-Crossing-based Least Squares (ZC-LS). In both methods, relative delays are extracted only within the local array and subsequently mapped to azimuth through a geometric least squares formulation; the approach is, therefore, distinct from distributed time difference-of-arrival (TDOA) localization. For comparison, the Multiple Signal Classification (MUSIC) algorithm is implemented as a covariance-based DOA estimator that operates without explicit delay extraction. Experiments were conducted using Loran-C transmissions from the Xuancheng, Xi’an, and Rongcheng stations, with 100 valid pulse groups collected for each station. Statistical analysis using boxplots shows that Corr-LS exhibits the largest variance due to broadened or shifted correlation peaks, particularly under skywave–groundwave interference. ZC-LS reduces both variance and bias by exploiting the deterministic zero-crossing structure of the Loran-C waveform. MUSIC produces the most concentrated azimuth estimates but requires a well-conditioned covariance matrix and substantially higher computational costs. The results demonstrate that ZC-LS achieves a favorable balance among angular accuracy, robustness, and real-time feasibility, making it suited for compact Loran-C receivers and complementary navigation applications in GNSS-challenged environments. Full article