Search Results (933)

Global Navigation Satellite System Reflectometry (GNSS-R), with its all-weather observation capability and low-cost advantage, provides an innovative solution for dynamic sea ice monitoring. In this paper, multi-dimensional features, including the GNSS-R Normalised Integrated Delay Waveform (N-IDW), the scattering coefficient and incidence angle derived from FY-3F satellite data, and the Delay Doppler Map (DDM) bistatic radar cross-section coefficient, are jointly used as model inputs. Experimental results show that this method successfully integrates FY-3F satellite data for sea ice thickness (SIT) retrieval, confirming the viability of employing FY-3F GNSS-R data for this purpose. An assessment of different algorithms in terms of their retrieval performance is conducted—covering RF, DT, KNN, SVM, ET, GBR, XGBR, and LR—and uses these eight models as base learners to construct different stacking models. After comparison, the ensemble stacking model using ET, LR, XGBR, and GBR as base models achieves the best retrieval performance. The MSE of this model for sea ice thickness retrieval reaches 0.0112 m, the RMSE reaches 0.1026 m and the correlation coefficient reaches 0.8876. Full article

Multi-sensor fusion of GNSS, IMU, and vision sensors has been extensively applied in urban Internet of Things systems and automated driving to improve positioning accuracy in complex environments. However, conventional FGO algorithms are based on fixed sensor weights, which limit their adaptability to fluctuations in sensor errors caused by environmental changes, thereby compromising positioning performance. To overcome this limitation, a novel multi-sensor adaptive weighted localization algorithm based on joint residuals detection was proposed in this study. The algorithm computes joint residuals by the sliding window accumulation of GNSS, IMU, and vision sensor measurements. By integrating a global weight decay factor into the M-estimation framework, the weights of each sensor were dynamically adjusted, thereby suppressing the effects of outliers on the state estimation. This approach enables high-precision and robust estimation of position, velocity, and attitude. Experimental results demonstrate that, based on validation with the GNSS–Visual–Inertial Navigation System (GVINS) public datasets sports field and complex environments, the proposed method exhibits superior performance in challenging low-altitude economic scenarios such as weak GNSS signals and significant IMU drift—specifically, it improves positioning accuracy by 32.3% and reduces velocity error by 32% compared to traditional FGO algorithms. In scenarios with GNSS signal interference, the system effectively mitigates error accumulation and maintains the stability of position and velocity estimation. The proposed algorithm demonstrates exceptional positioning accuracy and robustness in complex and dynamic environments, making it highly suitable for advanced urban IoT and automated driving applications. Full article

In complex urban environments, Global Navigation Satellite System (GNSS) signals are easily affected by building blockage and multipath effects, which may degrade positioning quality or even cause GNSS denial. As a result, conventional integrated navigation systems suffer from accumulated errors due to insufficient global constraints. To address this problem, a multi-source integrated positioning method incorporating semantic information is proposed. Fixed traffic lights are selected as semantic landmarks, and an object detection network is used to extract the center pixel coordinates and detection confidence of the landmarks. Then, by combining depth information, camera pose, and the prior global coordinates of fixed semantic landmarks, a semantic target inversion model is established to transform two-dimensional image information into three-dimensional position estimates in the world coordinate system. Semantic factors are further constructed and incorporated into backend factor graph optimization. To determine the weighting of semantic factors, the influences of pixel localization error, depth estimation error, camera pose error, and prior coordinate error of fixed semantic landmarks on semantic observations are analyzed, and a noise covariance model for semantic factors is established. Finally, an unmanned ground vehicle experimental platform is built to validate and analyze the proposed factor graph algorithm. The experimental results show that, under GNSS-degraded conditions, the algorithm with semantic factors can provide supplementary global constraints for the system and effectively suppress accumulated positioning errors. In Experiment 1, compared with the algorithm without semantic factors, the maximum absolute trajectory error is reduced by 46.26%. To further verify the applicability of the proposed method in more complex scenarios, Experiment 2 is conducted on a longer route with multiple semantic landmarks and a more severe GNSS-degraded interval. The results show that the proposed method reduces the maximum APE from 6.5432 m to 3.4778 m, corresponding to a reduction of approximately 46.85%. These results demonstrate that the proposed semantic factor can improve the robustness of multi-source fusion localization in GNSS-degraded environments. Full article

Integration of high-rate Global Navigation Satellite Systems (GNSS) with strong motion (SM) sensors enables accurate broadband coseismic displacements, which are critical for earthquake early warning and rapid source inversion. However, GNSS colored noise and SM baseline shift can degrade the accuracy and stability of the integrated displacements. In this study, we propose a novel loose integration approach where a two-step Kalman filter (KF) is used. In the first step, the high-rate GNSS displacements without colored noise are estimated using an adaptive KF that parameterizes the colored noise. Then, the denoised high-rate GNSS displacements are integrated with SM in the second KF where the baseline shift in SM is parameterized as a random walk process. The effectiveness of the proposed method was validated with co-located high-rate GNSS and strong motion data collected from a shake table experiment, the 2010 Mw 7.2 El Mayor-Cucapah earthquake, the 2016 Mw 7.8 Kaikōura earthquake, and the 2019 Mw 7.1 Ridgecrest earthquake. The results show that the proposed method achieves an RMSE of 1.1 mm, a 21% improvement over the KFb solution when shake table recordings are used as the reference. Application to three real earthquake cases demonstrates that the method effectively mitigates low-frequency GNSS noise and SM baseline shift, resulting in more accurate and stable coseismic displacement estimates. Full article

Accurate estimation of the weighted mean temperature (Tm) is essential for retrieving precipitable water vapor (PWV) from ground-based Global Navigation Satellite System (GNSS) observations. Machine learning (ML) techniques excel in modeling nonlinear relationships among Tm time series, station geographic coordinates, and surface meteorological parameters, and recent studies have demonstrated that ML and neural network models outperform conventional linear Tm models. However, the full potential of surface meteorological measurements at GNSS stations for high-precision Tm retrieval remains to be fully explored. This study develops regional Tm empirical models using two ML methods—random forest (RF) and Temporal Mixture of Experts with Sequential Attention (TMESA)—to generate reliable real-time Tm estimates and enhance the accuracy of operational GNSS-PWV retrievals over China. A traditional linear model is adopted as the baseline to evaluate the performance improvements of the proposed models. The models are trained and tested using 10-year (2014–2023) hourly ERA5-derived Tm products and in-situ surface pressure, temperature, and relative humidity from 2377 meteorological stations, with Tm diurnal variations, station coordinates, and day of year integrated as auxiliary predictive features. Validation is conducted using 2024 ERA5 reanalysis data and radiosonde profiles from 120 stations across China. Results show that the RF model yields a bias (RMSE) of −0.11 K (2.67 K) against ERA5 and −0.21 K (2.67 K) against radiosonde data, while the TMESA model achieves superior performance with bias (RMSE) of −0.02 K (2.34 K) and 0.09 K (2.46 K), respectively, whose performance levels comparable to state-of-the-art studies. Compared with the traditional linear model, the RF model reduces Tm RMSE by 32% against ERA5 and 25% against radiosonde data, while the TMESA model achieves reductions of 40% and 33%, respectively. These findings confirm that the proposed ML models can provide high-accuracy Tm estimates for reliable GNSS-PWV retrieval. Future work will focus on the operational application of these models for near-real-time GNSS-PWV estimation. Full article

Synthetic aperture radar (SAR) can acquire navigation data to correct inertial navigation system (INS) errors even under global navigation satellite system (GNSS)-denied conditions. However, when unmanned aerial vehicles (UAVs) may deactivate the SAR system to maintain radio silence, or the SAR sensor may be subjected to transient interference, the INS/SAR integrated navigation system transitions to degraded scenarios without SAR navigation data. Furthermore, the irregular sampling characteristics of SAR navigation data pose significant challenges to the localization performance of the INS/SAR integrated navigation system. In order to address the above challenges faced by UAVs, we propose a liquid time-constant (LTC) network-enhanced INS/SAR integrated localization method. The method adopts a loosely coupled integration strategy with training and prediction modes. During training, an LTC-assisted localization prediction network (LTC-ALPN) is designed to model input–output relationships using prior flight data while explicitly accounting for the non-uniform temporal sampling characteristics of SAR measurements. In prediction mode, the trained LTC-ALPN forecasts missing SAR navigation information, which is subsequently fused with INS outputs via a Kalman filter to maintain high-precision positioning during SAR outages. Experimental results demonstrate that, compared to pure INS localization in degraded scenarios, the proposed method reduces northward error MAE and RMSE by approximately 92.8% and 93.9% and eastward error MAE and RMSE by 54.1% and 67.1%. Against suboptimal network baselines, further improvements of 50.8%/38.1% (north) and 17.1%/16.7% (east) in MAE/RMSE were achieved. Full article

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

Landslide event warning in mining areas is essential for geohazard risk mitigation and infrastructure safety. With the increasing use of Global Navigation Satellite System (GNSS) monitoring networks, warning decisions are often derived from abnormal deformation responses in continuous displacement records. However, deriving stable and transferable warning decisions from GNSS networks is challenged by spatially coupled station responses, time-varying displacement patterns, and incomplete or disturbed observations. To address these issues, this study proposes a graph-augmented gradient boosting decision tree framework, termed GA-GBDT (Graph-Augmented Gradient Boosting Decision Trees), for multi-station landslide event warning in mining areas. The framework first constructs a weighted station graph to encode spatial dependence across stations. Based on this graph, a Gated Recurrent Unit (GRU) and a Graph Convolutional Network (GCN) are integrated to learn spatio-temporal embeddings, which are then fused with station-wise features and fed into XGBoost (eXtreme Gradient Boosting) for warning decision-making. Experiments on a 90-station GNSS network show that GA-GBDT outperforms representative rule-based, machine-learning, and deep-learning baselines, achieving more robust warning performance with improved generalization and false-alarm control. These results indicate that GA-GBDT improves warning robustness, decision stability, and cross-zone generalization for GNSS-based landslide warning in mining areas, with potential transferability to other slope warning scenarios. Full article

The Global Navigation Satellite System (GNSS) provides meter-level positioning in open environments, but its accuracy degrades severely in dense urban areas due to signal blockage and multipath effects. To address this problem, this paper proposes a hierarchical collaborative fusion positioning method based on GNSS, 5G, and the Inertial Navigation System (INS) with cross-source observation quality assessment. The proposed method integrates dual-domain error suppression, adaptive-shrinkage Unscented Kalman Filter (UKF) estimation, and observation-quality-aware adaptive weighting to mitigate systematic bias, random gross errors, and observation degradation. Unlike conventional fixed-weight or single-source-quality fusion schemes, the proposed method jointly combines gross-error detection, residual-driven covariance shrinkage, and adaptive weight regulation in a unified framework. Experiments were conducted in open outdoor, semi-occluded outdoor, and fully occluded indoor scenarios. The proposed method achieved a horizontal RMSE of 1.61 m in the semi-occluded outdoor environment. Compared with the the long short-term memory (LSTM)-aided UKF baseline, the positioning RMSE was reduced by 32.4%, and the positioning interruption rate was reduced by 49.5%. Full article

With the rapid development of low-altitude UAVs, accurate wind detection is crucial for ensuring flight safety and enabling broader applications. To address this need, this paper introduces a highly integrated CDWL system specifically designed for compact UAV platforms. The system incorporates a self-compensating Harris Hawks Optimization (SC-HHO) retrieval algorithm, which is tailored to the high-dynamic flight environment and stringent payload constraints of UAVs. This algorithm enables real-time wind retrieval with low dependence on external reference data while effectively compensating for platform motion. The performance of the proposed system was validated through the comparative experiment and the UAV-borne experiment. In the comparative experiment, the CDWL showed correlation coefficients above 0.976 in horizontal wind speed and 0.987 in horizontal wind direction relative to a benchmark airborne CDWL system, with corresponding root-mean-square errors better than 0.395 m/s and 4.135°, respectively. During the UAV-borne experiment, the CDWL retrieved platform velocity using the self-compensating mechanism, achieving a standard deviation of 0.080 m/s relative to global navigation satellite system (GNSS) measurements, and successfully acquired wind field information. These results confirm that the developed system provides a viable and practical technical solution for UAV-based remote wind sensing. Full article

This article presents the design, implementation, and experimental validation of an autonomous drone system for search and rescue operations in cluttered GNSS-denied environments. The proposed platform integrates advanced navigation, mapping, and victim-detection capabilities, leveraging a suite of RGB-D cameras and edge-AI computation for real-time perception and decision-making. A key contribution is the integration of an eye-blink-detection pipeline for onboard assessment of the consciousness states of detected victims, enabling the drone to prioritize rescue efforts based on victim alertness. The system employs a modular software architecture with a pipeline that combines a U-Net segmentation network with a MultiScaleLSTM classifier, achieving approximately 97.73% accuracy and a combined inference latency of 6.35 ms on the NVIDIA Jetson Xavier-NX. Experimental results demonstrate the drone’s ability to autonomously explore unknown environments, accurately detect and classify victims, and operate effectively in real-world scenarios. The article also discusses observed challenges, such as computational bottlenecks and false positive detections, and outlines future directions for improving system robustness and autonomy. Full article

In environments where GNSS signals are unavailable—such as indoor spaces, underground facilities, and forested areas—autonomous UAV navigation faces challenges related to localization uncertainty and limited onboard sensing capability. This study proposes a lightweight navigation framework using a single Intel RealSense D435i depth camera, integrating RTAB-Map SLAM, DWA-based local planning, and a direction-oriented frontier exploration strategy. The proposed exploration strategy introduces heading consistency into frontier target selection to support navigation in directionally constrained environments. The system is implemented within the ROS framework and evaluated in Gazebo/ArduPilot SITL simulation environments under low-, medium-, and high-density obstacle configurations. The results show that the system successfully completed autonomous traversal and return-to-home missions across all scenarios, with traversal RMSE values of 0.195 m, 0.197 m, and 0.420 m and return RMSE values of 0.295 m, 0.474 m, and 1.084 m, respectively. Qualitative dynamic-obstacle tests further demonstrate the system’s capability for local map updating and replanning. It should be noted that the current evaluation is primarily simulation-based and conducted in simplified environments. Therefore, the results are interpreted as initial system-level validation rather than full real-world deployment verification. The proposed system should not be directly interpreted as a ready-to-deploy real-world UAV navigation solution. Future work will focus on physical UAV experiments and more realistic GNSS-denied environments. Full article

Cooperative localization enables teams of robots to maintain better positioning in GNSS-denied environments by sharing state estimates and inter-robot range measurements to reduce the rate of proprioceptive odometry drift. In scenarios without fixed navigation beacons or pre-surveyed reference nodes, each robot functions as both a positioning client and a mobile ranging peer. A critical vulnerability in this architecture is silent fault propagation. A robot with a degraded localization solution may broadcast an incorrect, often overconfident position estimate, corrupting its peers’ localization. Classical Global Navigation Satellite System (GNSS) Receiver Autonomous Integrity Monitoring (RAIM) methods are ineffective in this context because meter-scale inter-robot separations introduce strong geometric nonlinearity and unstable Geometric Dilution of Precision (GDOP), resulting in scattered subset solutions rather than the coherent, biased clusters that RAIM is designed to detect. This paper addresses this vulnerability by proposing a two-stage distributed Fault Detection and Identification (FDI) architecture for peer-to-peer ranging-based cooperative localization. The first stage applies a global chi-square test on Weighted Least-Squares trilateration residuals to detect the presence of a fault. The second stage identifies the faulty robot by computing Leave-One-Out and Leave-Two-Out subset solutions, which are then partitioned using a clustering algorithm. The cluster that exempts measurements from the faulty robot is identified using either a maximum-cardinality or a minimum-variance criterion. A decentralized voting protocol that requires at least two independent corroborations is then employed for network-wide fault declaration. Monte Carlo simulations show that the clustering-based identification method outperforms classical residual-based methods across multiple fault types, with results reported for the planar (2D) case. No single clustering configuration dominates in terms of identification performance across all tested fault conditions, as performance varies with the fault profile. The proposed architecture operates fully in a distributed manner, requiring only the exchange of position estimates, covariances, and binary votes. Full article

This study is conducted to investigate the deformation evolution and collapse mechanism of the Rongxing gypsum mine by integrating multi-source monitoring data, including synthetic aperture radar (SAR), global navigation satellite system (GNSS), and microseismic observations. Long-term surface deformation from 2015 to 2025 is retrieved using small baseline subset interferometric synthetic aperture radar (SBAS-InSAR), while GNSS data (2021–2022) are used to capture rapid ground displacement during the collapse event. Microseismic monitoring provides insights into the evolution of subsurface fracturing processes. The results show that the pre-collapse stage is characterized by continuous and spatially heterogeneous subsidence. Prior to the collapse, microseismic activity is observed to exhibit clear precursory signals, including an increase in event number, a decrease in b-value, and accelerated cumulative energy release, suggesting that the transition from distributed microcrack development to large-scale fracture coalescence is occurring. The b-value, derived from the Gutenberg–Richter frequency–magnitude relationship, describes the relative proportion of small to large seismic events and reflects variations in the statistical distribution of event magnitudes. During the collapse stage, abrupt, large-magnitude subsidence is observed by GNSS. After the collapse, the deformation is found to enter a long-term adjustment phase characterized by the coexistence of subsidence and uplift, indicating that stress redistribution within the overburden is occurring. Based on these observations, a conceptual model is proposed to describe the progressive failure mechanism of the goaf, with four stages: slow subsidence, accelerated deformation, collapse, and post-collapse adjustment. This study demonstrates the effectiveness of integrating SBAS-InSAR, GNSS, and microseismic monitoring for understanding the full lifecycle of goaf collapse. It provides valuable insights for early warning of mining-induced geohazards. 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