Search Results (1,335)

Global Navigation Satellite Systems (GNSSs) are playing an increasingly important role in monitoring climate change, providing precise and continuous data on processes occurring in the atmosphere, hydrosphere, cryosphere, biosphere, and lithosphere. Initially, GNSSs were used primarily for navigation and geodetic purposes, but the development of satellite signal-processing methods has significantly expanded their applications. This paper presents an overview of climate research with particular emphasis on GNSS-RO, PPP, CORS, GNSS-R, and GNSS-IR techniques. The paper discusses the possibilities for monitoring atmospheric water vapor content, sea-level changes, snow cover, glaciers, soil moisture, vegetation status, and crustal deformation associated with redistribution of the Earth’s mass induced by climate change. The analysis indicates that GNSS observations are currently an important data source for climate and environmental research, as well as in weather forecasting systems, environmental monitoring, and geodynamic analyses. Integration of GNSS data with other remote sensing techniques supports a more comprehensive assessment of changes occurring in the Earth’s climate system as well as supporting the development of methods for adaptation to ongoing climate change. Full article

Postseismic deformation provides fundamental insights into earthquake-cycle processes, lithospheric rheology, and stress redistribution following large earthquakes. Although the 2020 Mw 7.0 Samos earthquake has been extensively investigated in terms of coseismic deformation and early postseismic behavior, the long-term evolution of deformation following the event remains poorly constrained. This study characterizes the long-term spatiotemporal evolution of postseismic deformation associated with the 2020 Mw 7.0 Samos earthquake using combined campaign and continuous GNSS observations. A total of 18 GNSS stations were analyzed over an approximately 4.5-year period following the earthquake. Pre-earthquake GNSS velocities were incorporated as prior constraints, while postseismic deformation was modeled using linear, logarithmic, exponential, and combined logarithmic–exponential functions. The preferred model for each station component was identified using the corrected Akaike Information Criterion (AICc), and model-selection robustness was evaluated through 1000 Monte Carlo observation–perturbation simulations. The results reveal a spatially heterogeneous postseismic deformation field with station-dependent temporal behavior. Among the nonlinear solutions passing the Monte Carlo and goodness-of-fit criteria, the seven single-process LOG and EXP solutions yielded characteristic relaxation times ranging from 182.6 to 730.5 days, with a median of 438.3 days. The two LOGEXP solutions additionally contained a logarithmic timescale of 109.6 days and exponential timescales of 292.2–438.3 days. These findings demonstrate that campaign GNSS observations, when integrated with continuous GNSS data and an objective statistical framework, can provide meaningful constraints on the long-term evolution of postseismic deformation despite sparse temporal sampling. More broadly, the results emphasize the strongly time-dependent nature of postseismic deformation and the critical role of observation timing in capturing its spatiotemporal evolution. Full article

The adoption of standalone Engine Control Units (ECUs) in motorsport and research vehicles introduces severe interoperability challenges with OEM electronic architectures, often rendering factory instrument clusters inoperative. Existing commercial displays address this but feature high costs, closed ecosystems, and limited extensibility. This paper presents a low-cost, software-defined automotive instrument cluster and telemetry platform built on a Raspberry Pi 4, bridging heterogeneous vehicle subsystems without modifying ECU firmware. The architecture unifies high-rate CAN bus acquisition, analog digitization, discrete chassis signal monitoring, and GNSS telemetry. To evaluate network expansion, an auxiliary Exhaust Gas Temperature (EGT) loop using a MAX31855 amplifier was integrated to broadcast thermal data over CAN to a Link G4+ ECU. A priority-scheduled multi-threaded software model ensures bounded application-level acquisition alongside graphical visualization and an onboard time-series database (TSDB) with an embedded Grafana observability framework. Experimental validation on a cross-manufacturer testbed (Mazda RX-8 with a Nissan SR20DET engine and Link G4+ ECU) demonstrated stable operation during long-duration testing. Stress-testing up to 1000 frames/s with no frame loss was observed. The results prove the platform provides a highly flexible, extensible, and economically accessible open-source alternative to proprietary motorsport instrumentation. Full article

The accuracy and consistency of error-state Kalman filtering for SINS/GNSS integrated navigation depend critically on properly tuned process and measurement noise covariance matrices. In dynamic operation, these covariances can be non-stationary: inertial uncertainty changes with maneuver intensity, vibration, and sensor-bias instability, whereas GNSS measurement quality changes with satellite geometry, multipath, obstruction, and signal loss. Fixed-covariance and classical adaptive filters can therefore become overconfident or insufficiently responsive during abrupt maneuvers and degraded GNSS reception. We propose a physics-informed constrained neural covariance estimation (PC-NCE) framework that augments, rather than replaces, the error-state Kalman filter (ESKF) by estimating bounded process and measurement covariance-scale parameters online. The framework maps IMU-window sequences, GNSS-quality indicators, innovation statistics, and motion-state descriptors through a CNN-BiLSTM-attention network to filter-admissible Q_k and R_k parameterizations injected into a closed-loop ESKF. Training enforces positivity, bounds, temporal smoothness, and innovation–consistency regularization. In a reproducible filter-level MATLAB scenario suite, PC-NCE improved covariance-scale tracking and selected consistency ratios relative to fixed and unconstrained neural baselines, whereas position RMSE gains were scenario-dependent. These results provide a simulation-level proof of concept supplemented by an initial held-out measured-trajectory evaluation; broader validation using a full 15-state SINS/GNSS implementation and additional field datasets remains necessary. By treating neural networks as uncertainty-perception layers rather than black-box state estimators, PC-NCE retains the interpretability and engineering safeguards of classical Kalman filtering. Full article

This work presents the design, construction and operation of the SKD-1, a modular skid-steer unmanned ground vehicle (UGV) developed as a low-cost research platform for mobile robotics applications. The platform integrates a differential skid-steer drive system, a Raspberry Pi-based onboard computer, and a microcontroller-based control layer implemented using an STM32 microcontroller. The sensing system includes light detection and ranging (LiDAR), global navigation satellite system (GNSS), and an inertial measurement unit (IMU), enabling experiments in localization, mapping, and autonomous navigation. The software architecture is based on the Robot Operating System (ROS) 2 framework, relying on standard ROS 2 packages for perception, mapping, and path planning, with the custom hardware-interface layer being the only non-standard software component. The mechanical and electronic subsystems were designed with a modular architecture that facilitates maintenance, sensor replacement, and hardware upgrades. The primary contribution of this work is the open-hardware design, integration, and documentation of a reproducible robotics testbed, motivated by the prohibitive cost of commercial platforms in resource-constrained research contexts. Indoor and outdoor experiments—covering velocity-tracking, SLAM, and waypoint-navigation trials—demonstrate the functional integration of the sensing, actuation, and computing subsystems. Full article

Road markings support lane guidance, traffic regulation, and machine perception, but their field inspection still depends largely on manual surveys or local retroreflectivity measurements. This study presents a vehicle-mounted inspection framework that fuses LiDAR intensity with camera-based semantic segmentation for automated road marking wear assessment. The platform integrates LiDAR, a stereo camera, IMU, GNSS, and an industrial computer. LiDAR-inertial mapping provides spatial alignment, while ground filtering, region-of-interest extraction, and adaptive intensity thresholding generate preliminary marking candidates. A YOLOv11 segmentation model produces pixel-level marking masks, and LiDAR candidates are projected onto the image plane for semantic confirmation. Confirmed points are accumulated into grid cells and evaluated using reflectance, point density, fusion retention, and geometric coverage indicators. In a representative route, 2441 grid units were analyzed: 1210 units were valid for formal grading, with 1060 good, 131 slightly worn, and 19 moderately worn units; 1231 units were reserved for review. The mean composite score of the valid grids was 0.906. A five-report aggregate further showed that 87.2% of the units received valid wear categories. The results indicate that multimodal fusion transforms road marking inspection into a quantitative, spatially referenced, and reportable process. Full article

Reservoir impoundment can reactivate pre-existing landslides and reorganize slope topography, thereby changing seepage conditions and subsequent deformation. However, crack mapping, geomorphic interpretation, and hydrodynamic diagnosis are still often treated as separate tasks. This study investigates the Hemenkou (HMK) landslide in the Wudongde Reservoir area, China, using multi-scale space–air–ground observations, including multi-temporal optical satellite images, unmanned aerial vehicle (UAV) photogrammetry, pyramid scene parsing network (PSPNet)-based crack segmentation, global navigation satellite system (GNSS) monitoring, and convergent cross mapping (CCM). The remote sensing record shows a progressive damage sequence: cracks were mainly restricted to the upper source area in 2012, crown cracking intensified and propagated downslope by December 2020, and the UAV survey of 10 June 2024 revealed a mature tension-crack network concentrated in Zone II. ResNet-50-PSPNet achieved the best crack-extraction performance among the tested models, with Precision = 0.9120, Recall = 0.9041, F1 = 0.9081, and IoU = 0.8316. The mapped cracks are dominated by short, narrow, northeast–southwest-oriented tension cracks. GNSS monitoring reveals strong spatial heterogeneity, with stepwise deformation concentrated in Zone II. CCM provides strong directional evidence for the influence of reservoir water-level fluctuation on Zone II deformation, whereas the weaker rainfall signal is consistent with a secondary reinforcing role. The apparent increase in the rainfall-related CCM signal from 2021 to 2023 is consistent with progressive crack expansion and potentially enhanced hydraulic connectivity in Zone II. Taken together, these observations support the interpretation that post-deformation topography, particularly the tension-crack network and disturbed toe, may organise preferential seepage pathways and increase the sensitivity of the landslide to reservoir drawdown. The study provides an integrated remote sensing and monitoring framework for process-based interpretation of reservoir landslides. Full article

Accurate, comprehensive, and spatially continuous monitoring of mining-induced surface subsidence is essential for geohazard prevention, ecological restoration, and safe mining. Conventional approaches, however, are limited by the sparse spatial distribution of GNSS observations, the difficulty of InSAR in resolving large and rapidly evolving deformation, and the reliance of probability integral method (PIM) calibration on sparse observations. Here, we evaluate an integrated UAV-LiDAR–PIM workflow for basin-wide characterization of mining-induced subsidence and improved spatial constraint of PIM parameters. We use the 3206 working face in an Ordos mining area as a case study. DEM differencing of multi-temporal UAV-LiDAR observations characterizes the spatial distribution of the subsidence basin. Uniform sampling within the affected zone then provides a high-density dataset for PIM calibration, which we compare with conventional profile-based monitoring. The two UAV-LiDAR DEM epochs yielded vertical quality-control RMSEs of 0.052 and 0.050 m, respectively, with a mean of 0.051 m. These results support their use for basin-scale deformation analysis. The inverted angular parameters indicate a larger mining influence extent along the dip direction than along the strike. Surface movement followed initial, active, and declining stages over 423 days, with a maximum subsidence rate of 87.39 mm d⁻¹ during the active stage. The integrated workflow reduces the spatial-sampling limitations of profile-based monitoring and adds two-dimensional constraints on basin geometry and directional variation. Combining basin-wide UAV-LiDAR observations with conventional profiles may improve site-specific PIM calibration and support mining-hazard assessment and ecological-restoration planning. Full article

Driven by the escalating global agricultural workforce shortage and the urgent need to meet the “Zero Hunger” mandate, the automation of harvest–transport workflows has emerged as a cornerstone of Agriculture 4.0. This paper highlights the latest research progress in multi-robot collaborative logistics scheduling across highly unstructured farming environments, underpinned by cutting-edge spatial perception and digital twin frameworks. Initially, we summarize the technological leap from conventional 2D geometric mapping to multi-modal semantic 3D reconstruction—fusing light detection and ranging (LiDAR), unmanned aerial vehicle (UAV) imagery, and spatial data—to enable high-fidelity forward-looking predictions. The discussion then transitions to algorithmic advancements, emphasizing the shift from traditional centralized operations research to decentralized, data-driven approaches such as Multi-Agent Reinforcement Learning (MARL). We also explore micro-kinematic predictive control mechanisms and the growing integration of ecological sustainability metrics into routing models. To demonstrate practical engineering progress, multi-agent implementations are analyzed across three typical spatial settings: high-throughput continuous relays in open fields, global navigation satellite system (GNSS)-denied discrete routing in dense orchards, and close-proximity human–robot collaboration (HRC) in smart greenhouses. Finally, we identify the remaining barriers to the large-scale commercialization of Agricultural Cyber-Physical Systems (ACPS), such as the “Sim-to-Real” gap restricted by edge-computing capacities, unclosed economic loops, and HRC ethical dilemmas, offering a forward-looking roadmap for next-generation resilient agricultural networks. Full article

To address Global Navigation Satellite System (GNSS) spoofing threats to Inertial Navigation System (INS)/GNSS integrated navigation systems, this paper analyzes the internal error propagation mechanisms and quantifies perturbation patterns within the Kalman filter (KF) architecture. Mathematical models for step-type, linear ramp, and nonlinear smooth ramp spoofing are established, and the Anomaly Signal-to-Noise Ratio (ASNR) is adopted to quantify disturbances across four core filtering dimensions based on real-world vehicular test data. The results demonstrate that filtering innovations at the forefront of information fusion respond most directly and sensitively (peaking at an ASNR of 341.4181) with a standard zero-mean Gaussian baseline, serving as the optimal metric for spoofing detection; in contrast, error states exhibit marked amplitude attenuation (maximum ASNR of 116.3291), while filter gains and state covariance show negligible variations (maximum ASNRs of 22.6023 and 19.3014, respectively). Further evaluation of Inertial Measurement Unit (IMU) accuracy constraints reveals that under step spoofing, position innovations remain robust (ASNR: 260–510), whereas velocity innovation ASNR drops by approximately 50% with IMU degradation; under linear ramp spoofing, velocity innovations dominate the response (ASNR: 41.16–70.46) while position innovations decay markedly; and under nonlinear smooth ramp spoofing, overall innovations are suppressed, and low-grade IMUs suffer severe noise masking (peak horizontal ASNRs dropping below 10), significantly enhancing attack stealthiness. The findings provide quantitative empirical evidence and theoretical guidance for anti-spoofing design in integrated navigation. Full article

Networks of permanent GNSS (Global Navigation Satellite System) reference stations generate large volumes of observation and ephemeris files, as well as the position estimates and quality indicators derived from them. These data are still typically stored as structured files, which makes them difficult to query, trace and integrate with geographic information systems (GIS). To provide an alternative, this article presents GeoGNSS-PS, a PostGIS-based information system for permanent GNSS stations that treats positioning results, processing origin and spatial geometry as first-class entities within a normalised relational schema. Estimated positions are stored as three-dimensional geometries in the ECEF (Earth-Centered Earth-Fixed) reference frame. Each record is identified by station, date, constellation and estimation method, whilst also storing other data such as the equipment at each station, the reference frame and epoch of its published coordinates, and the software and ephemeris product used in each run. The schema and its analytical functions in SQL (Structured Query Language) are initialised from a single declarative description. The system is evaluated using twenty-eight stations from the Spanish Network of GNSS Reference Stations (ERGNSS, as it is known in Spanish) every day throughout the year 2025, producing 190,725 daily positions in 74 MB. In addition, five compact SQL queries express patterns for analysing thes data. The system is compared against a file-based baseline, another alternative spatial database hosting the analytical core of the same schema, and against synthetic datasets of up to ${10}^7$ rows. The proposed system excels in aggregations built on spatial primitives and in client memory, whilst the alternative spatial database matches its performance times but is unable to express several of the patterns in SQL. Full article

Canopy occlusion, dynamic vegetation, structural degeneracy, and implicit terrain risks make stable localization and task-adaptive planning difficult for resource-constrained orchard robots. This paper proposes CitraNav, a lightweight navigation method for global navigation satellite system (GNSS)-denied orchards. It separates stable geometric evidence for localization from short-lived semantic evidence for planning, preventing semantic observations from accumulating in the global map. For localization, a hierarchical voxel map selects its resolution according to local structure and light detection and ranging (LiDAR) sampling characteristics, while cross-frame reliability and observability constraints suppress updates from transient vegetation and weakly observable directions. For planning, synchronized color and depth observations form a local semantic risk point cloud. A model predictive path integral (MPPI) planner combines task-dependent semantic costs with exact-footprint collision checking against currently detected obstacles. In simulation, CitraNav achieved a mean translational localization root mean square error (RMSE) of 0.075 m. Compared with geometric point-cloud planning, semantic planning reduced the collision rate by 71.4% and increased weed coverage 4.72-fold. Across 14 real-world sequences spanning farm-road, lawn, forest, and orchard environments, CitraNav achieved mean translational and heading RMSEs of 0.151 m and 1.13°, respectively, while using 72.3–87.4% fewer geometric map cells than the comparison methods. The complete perception–planning pipeline operated at 20.3–32.7 frames per second on an edge platform. These results suggest that CitraNav offers a balanced approach to localization stability, task-adaptive planning, and computational efficiency in complex orchard navigation. Full article

Integrated Water Vapor (IWV) is a key indicator of atmospheric moisture, and its accurate quantification requires precise characterization of the errors inherent to each observing system, a task complicated by the absence of a true, error-free atmospheric reference. This study characterizes the absolute error structures of IWV retrievals from Global Navigation Satellite Systems (GNSS), Microwave Radiometers (MWR), and Radiosonde using a temporally collocated dataset of 326 trivariate samples acquired in Nicosia, Cyprus over a complete annual cycle (March 2025–March 2026). Triple Collocation Analysis (TCA) and the Three-Cornered Hat (TCH) method were applied without assuming a ground truth, with non-parametric bootstrap resampling (10,000 iterations) used to derive 95% confidence intervals and flag statistically unstable estimates. During the dry season, the recovered error ordering was radiosonde (0.38 kg m⁻²) below MWR (0.60 kg m⁻²) and GNSS (1.69 kg m⁻²), which is consistent with the a priori budget. The MWR error rose sharply to 5.79 kg m⁻² in the wet season and to 8.91 kg m⁻² in the highest moisture bin, consistent with degradation of the K-band retrieval in the presence of liquid water. The wet season value is a lower bound, since any positive error covariance between the radiometer and the radiosonde acts to increase it, whereas the GNSS error approximately doubled to 3.53 kg m⁻², indicating comparatively greater resilience across weather regimes. The radiosonde error, lowest of the three sensors in dry conditions (0.38 kg m⁻²), could not be reliably resolved in the wet season (98.8% truncation), the lowest moisture bin (71.4%), or the highest moisture bin (94.5%), and it is reported as unstable in each. These results indicate a substantial degradation in radiometric retrieval accuracy and identify the specific regimes in which trivariate error decomposition itself becomes statistically unreliable. Full article

Smartphone GNSS metadata remains an underexplored source for evaluating navigation-signal integrity in real-world conditions. We investigated GPS behavior recorded by a Samsung Galaxy A72 smartphone across two European flights (EXP03: Rome–Bucharest, n = 377; EXP10: Bucharest–Lisbon, n = 78) and 275 terrestrial reference photographs (Cabo da Roca, Portugal). A total of 730 photographs were analyzed using a seven-indicator taxonomy, conceptually inspired by Receiver Autonomous Integrity Monitoring (RAIM) principles, covering anti-spoofing, anti-sniffing, and anti-tampering checks. GPS capture rates reached 100% (Timestamp Camera) and 83.3% (native camera) up to 11,439 m WGS84, among the highest EXIF altitude profiles reported to date. Velocity spikes (1560–1875 km/h), one at cruise altitude and one during landing, were indistinguishable from GPS spoofing at the EXIF level, and their physical origin is undetermined. Phantom geolocation was absent in flight (0/442) versus 37.3% on the ground, consistent with GPS constellation visibility as the primary factor. In total, 88% (n = 920) of JPEG files lacked the standard EOI marker, generating false positives in integrity validators. Findings are device-specific, derived from non-independent observations, and require replication before generalizing to other GNSS receivers or latitudes. The framework offers a methodological basis for EXIF integrity analysis relevant to EU AI Act Article 10(3) data quality and UAV precision-agriculture georeferencing. Full article

To address the challenges of GNSS signal blockages and severe multipath effects in complex urban environments, this paper proposes a tightly coupled precise point positioning (PPP)/inertial navigation system (INS) integration method based on an adaptive robust Kalman filter (ARKF) for smartphones. The proposed method integrates a robust estimation module based on the IGG-III weight function and an adaptive factor derived from vehicle dynamic intensity and geometric precision indicators, to mitigate observation outliers and dynamic model errors. To evaluate the positioning performance of this algorithm, two typical vehicle experiments based on the GNSS and inertial measurement unit (IMU) chipsets of the Xiaomi Mi 8, as well as an external H30 IMU, were conducted. Experimental results show that in the urban expressway environment, the horizontal root mean square (RMS) error of the loosely coupled PPP/INS solution was reduced by 74.17% compared with the conventional PPP solution, while the maximum horizontal positioning error of the tightly coupled PPP/INS solution was reduced by 44.82% compared with the loosely coupled PPP/INS solution. In the complex urban road and tunnel environments, the proposed ARKF-based tightly coupled PPP/INS method achieved a 36.79% reduction in horizontal RMS error compared with the tightly coupled PPP/INS solution based on the standard extended Kalman filter (EKF) and demonstrated more robust positioning performance in the tunnel. Full article