Search Results (3,452)

Slow earthquakes represent an important mode of fault slip transitional between stable creep and dynamic rupture. Their occurrence is jointly controlled by mineral composition, pore-fluid pressure, effective normal stress, system stiffness, and microstructural evolution. Because the internal state of natural faults cannot be directly observed, studies of slow slip, tectonic tremor, and low-frequency earthquakes have long been challenged by weak signals, complex noise, and discrepancies in observational scales. Building on the physical foundations of rock friction and slip stability, this review summarizes recent applications of deep learning to laboratory friction and acoustic data, natural seismic waveforms, Global Navigation Satellite System (GNSS) observations, and strain measurements, with particular emphasis on event detection, fault-state estimation, rate-and-state friction parameter inversion, and forecasting of slip evolution. Existing studies have progressed from event identification to the reconstruction of shear stress, estimation of frictional parameters, and prediction of future fault states. Nevertheless, applications to natural faults remain dominated by event detection and catalog construction, whereas parameter inversion and forecasting still rely largely on laboratory experiments or synthetic data. Physics-informed neural networks, transfer learning, reduced-order modeling, and data assimilation provide promising pathways for integrating laboratory experiments, numerical simulations, and natural observations; however, their reliability remains limited by constitutive-model dependence, parameter non-uniqueness, domain shift, and insufficient independent validation. Future work should strengthen multi-observation integration, cross-region validation, and uncertainty quantification, while developing a bidirectional framework linking laboratory experiments, numerical simulations, and natural fault observations. At present, deep learning is better suited to fault-state characterization and probabilistic assessment of slip trends than to deterministic prediction of the exact timing of slow earthquakes. Full article

Accurate stereo visual–inertial measurement is essential for mobile robots operating in Global Navigation Satellite System (GNSS)-denied and structurally complex environments. In stereo visual–inertial odometry (VIO), pose and trajectory outputs depend strongly on the point measurements delivered by the visual front end before sensor-fusion update. In sparse-texture but structurally regular scenes, tracked point features may exhibit poor persistence, uneven spatial distribution, and local tracking noise, even when informative line structures are present. Existing point–line VIO methods can improve positioning accuracy by introducing line landmarks or line residuals, but they usually modify the estimator state, measurement model, and Jacobian treatment. We present a scene-adaptive line-aware visual measurement conditioning method for stereo VIO front ends with point-measurement updates. The method uses 2-D image-line segments as lightweight structural priors and applies bounded normal-direction conditioning to reliable point measurements before a fixed-interface VIO back-end update. A sparse pruning safeguard removes only highly inconsistent long-lived tracks under strong structural support, while a scene-level confidence gate attenuates the intervention when line evidence is weak or unstable. The method is instantiated and evaluated in an S-MSCKF pipeline. On the reported EuRoC MAV sequences, it reduces the sequence-averaged absolute trajectory error (ATE) RMSE by approximately 13% relative to S-MSCKF, with 3–27% reductions on machine-hall sequences. On three real-world robot measurement sequences with an RTK-aided inertial reference, the mean Sim(2)-aligned planar position error decreases from 8.72 m to 7.31 m, and the mean yaw error decreases from $8.{02}^{\circ }$ to $6.{76}^{\circ }$; an additional scale-preserving SE(2) evaluation reveals sequence-dependent planar behavior and residual metric-scale sensitivity. Candidate-level stereo-consistency diagnostics show subpixel mean and 95th-percentile image-domain perturbations without systematic vertical-stereo bias, while the final reliability-weighted primary-view update is analytically bounded by approximately 0.221 pixels in the reported implementation. Runtime profiling reports an average front-end time of 33.34 ms on the tested CPU platform, close to the 33.3 ms frame period of the 30 Hz stereo input, although the $\mu +3\sigma$ runtime of 47.23 ms exceeds a strict frame-by-frame 30 Hz budget. These results suggest that line-aware front-end conditioning can improve visual measurement quality in structured stereo visual–inertial sensing without modifying the evaluated back-end interface. Full article

Global Navigation Satellite System (GNSS)-based measurements are widely used for sports monitoring and outdoor activity analysis; however, consumer-grade smartphones often produce degraded trajectories, inaccurate elevation profiles, and unreliable pace estimates. This study proposes a multi-stage framework for reconstructing low-fidelity GNSS running trajectories using an averaged high-fidelity wearable GNSS reference proxy. The framework combines activity-window selection, trajectory filtering and route-consistent projection, reference-based elevation correction, and pace reconstruction using two complementary approaches: a Linear Acceleration Influence Model and a Physics-Based Model. The methodology was validated using three high-fidelity and three low-fidelity recordings collected on a shared 5.726 km route. Within the common activity window, raw low-fidelity observations had a pooled nearest-route RMSE of 30.72 m, whereas retained route-consistent assignments had a residual RMSE of 14.84 m. Aggregate pace agreement improved from 2.40 to 1.74 min/km RMSE and from 32.84% to 25.46% MAPE. Raw smartphone elevation had a pooled RMSE of 190.27 m relative to the adopted reference profile, supporting reference-based elevation substitution. A constant-velocity Kalman RTS baseline reduced positional RMSE from 30.72 to 29.05 m (5.4%), whereas the complete route-association procedure eliminated severe backtracking that remained after distance thresholding alone. The proposed framework provides a transparent and reproducible solution for reconstructing sparse consumer-grade GNSS activities while preserving explicit uncertainty. Full article

Ground-based Global Navigation Satellite System (GNSS) stations provide continuous atmospheric remote sensing through electromagnetic propagation delays. Precise point positioning (PPP) yields zenith tropospheric delay (ZTD), and ZTD gradients provide a proxy for off-farm tropospheric structure that is unavailable to supervisory control and data acquisition (SCADA)-only forecasts. We propose a model combining a long short-term memory (LSTM) backbone, GNSS conditioning, and a graph neural network (GNN), denoted LSTM+GNN+GNSS, for 4 h wind-power forecasting. Historical PPP-derived ZTD and quality indicators condition a shared temporal representation; a ZTD-gradient direction proxy, turbine geometry, and observation confidence guide a gated graph-residual correction at 15–90 min. On 666 common Yandun test origins, we compare LSTM, LSTM+GNN, and LSTM+GNN+GNSS. The complete system achieves a normalized mean absolute error (nMAE) of 4.53% (4.527 ± 0.132% across three power-model seeds), reducing nMAE by 7.57% relative to LSTM+GNN and 8.23% relative to LSTM. Paired moving-block 95% confidence intervals support both comparisons, while Bonferroni-adjusted lead-wise tests agree from 30 to 225 min. These results demonstrate the incremental predictive value of the complete GNSS-conditioning pathway under the chronological holdout protocol. Full article

In Global Navigation Satellite System (GNSS)-denied deep-sea environments, traditional scalar gravity matching navigation methods frequently suffer severe performance degradation in weak-feature, highly repetitive gravity anomaly regions. Inspired by the hippocampal spatial memory mechanism and natural graded foraging behavior of benthic marine organisms, this paper proposes a full-chain bionic framework named the Physics-Consistent Multi-Scale Adaptive Particle Filter for Gravity Matching Navigation (PC-MAPF-GM). This method endows the particle filter with four layers of biologically mimicked autonomous regulation capabilities: quantitative gravity field local suitability assessment, dynamically adjusted time-varying search scope, three-level multi-scale stepwise matching, and along-track trajectory motion physics consistency constraint. The verification of long-term shipborne lake experiments confirms that the proposed method reduces the final gravity matching positioning root mean square error (RMSE) to only 528.2 m, which is more than 41% lower than the classical terrain contour matching (TERCOM) benchmark and 31% lower than iterative closest contour point (ICCP). This biomimetic full-design-chain solution provides a robust new practical navigation paradigm for long-endurance fully autonomous underwater vehicles operating without any external auxiliary positioning information. Full article

Noncollocation can obscure small constellation-dependent differences in Global Navigation Satellite System (GNSS) precipitable water vapor (PWV). We assessed five constellation configurations processed with a common precise point positioning strategy at the same four physical station groups surrounding the Beijing radiosonde site during June–September 2023–2025. In the original year-specific matched samples, BeiDou Navigation Satellite System (BDS)-only Root Mean Square Error (RMSE) was 1.3%, 5.4%, and 7.9% lower than GPS-only RMSE in 2023, 2024, and 2025, respectively. Because these percentages were descriptive, we added a direct paired analysis on strict-common samples (n = 225, 231, and 216). Paired BDS-minus-GPS RMSE differences were −0.19 mm (95% CI: −0.41 to −0.03), −0.39 mm (−0.61 to −0.16), and −0.44 mm (−0.65 to −0.23), respectively. Fixed-geometry nearest-station, alternative inverse-distance weighting, and elevation-sensitive tests showed that the small constellation difference was not invariant to spatial transfer. Residuals became progressively more negative with increasing radiosonde PWV, and high-moisture (>50 mm) biases were −10.75 mm for BDS and −11.36 mm for GPS. Full-season leave-one-year-out PWV percentile achieved Area Under Curve (AUC) = 0.846, but its transferred threshold yielded False Alarm Ratio (FAR) = 92.2% and Critical Success Index (CSI) = 7.6%. The common five-configuration processing provides a controlled comparison; the rainfall result supports retrospective regional moisture monitoring, not stand-alone operational prediction. Full article

Ionospheric scintillation and radio frequency interference (RFI) affect Global Navigation Satellite System (GNSS) signals, making their distinction important for ionospheric monitoring and interference detection. This study identifies GNSS signal anomalies associated with scintillation and RFI using COSMIC-2 1 Hz precise orbit determination (POD) observations. Using 60 s per-satellite windows, dual-frequency time-series inputs and 16-dimensional statistical features were extracted, and weak labels for Normal, Scintillation, and RFI were generated from the amplitude scintillation index S₄ and the RFI index. An InceptionTimeLite-FiLM-DeepSets joint multi-satellite model, where FiLM denotes feature-wise linear modulation, was trained and tested on 2024 data and directly applied to the full-year 2025 dataset. On the 2024 test set, recall for Normal, Scintillation, and RFI was 98.9%, 76.1%, and 58.5%, respectively, with a Macro-F1 of 0.8227 and a Matthews correlation coefficient of 0.7120. Predicted Scintillation occurrence rates were higher at low magnetic latitudes and during the postsunset premidnight period, whereas predicted RFI occurrence rates were concentrated over North Africa, the Middle East, South Asia, and Southeast Asia. These results show that, within the weak-label framework, COSMIC-2 1 Hz POD observations can support GNSS signal anomaly classification and spatiotemporal distribution analysis, providing a complementary approach for long-term anomaly analysis. Full article

Clock steering, the core time-frequency technology for high-precision single-satellite time reference generation in global navigation satellite systems, can effectively combine the excellent short-term stability of Oven-Controlled Crystal Oscillators (OCXOs)—whose top performance indicators have already surpassed many space-borne atomic clocks in recent years—with the superior long-term stability of atomic clocks, to obtain time signals with optimal full-range stability. This paper proposes a novel clock steering scheme that integrates an adaptive variational Bayesian Kalman filter and a Proportional-Integral-Derivative (PID) automatic controller: the filter constructs a separable variational approximation for the joint posterior distribution of clock states and measurement noise parameters, to achieve real-time adaptive estimation of noise at each timestamp, while the PID controller performs closed-loop fine adjustment on the output frequency. Comparative simulations with the classic Linear Quadratic Gaussian (LQG) control scheme verify that the proposed method can generate steered time signals with better stability performance in both short-term and long-term dimensions. This work further investigates the influence of measurement noise at different intensity levels on clock steering performance and conducts corresponding mechanism analysis supported by quantitative data. The proposed scheme and conclusions can provide a reliable reference for selecting appropriate clock steering strategies under different noise conditions. Full article

Global Navigation Satellite Systems (GNSS) positioning accuracy is affected by multiple error sources, including atmospheric delays, multipath propagation, and receiver noise, which can significantly reduce positioning reliability in low-cost receivers. This study investigates the use of a feedforward neural network to compensate for positioning errors in a static GNSS receiver scenario. A synthetic dataset was generated in MATLAB/Simulink by simulating positioning perturbations around a known reference location. Consecutive coordinate differences were used as input features, and a compact feedforward neural network with 45 hidden neurons was trained using the Levenberg–Marquardt algorithm to estimate positioning error components. The proposed approach was evaluated through residual error distribution, regression, temporal dispersion, and spatial scatter analyses. The results indicate that, for the primary 10 m error scenario, neural network-based compensation reduced temporal dispersion by approximately 46% and produced a more compact spatial distribution of corrected positions around the reference location. The residual errors remained concentrated near zero, indicating improved positioning consistency under the investigated simulation conditions. Sensitivity analysis across nominal error radii of $R_{95}$ = 1, 5, 10, 15, and 20 m showed consistent reductions in both RMSE and standard deviation for radii of 10 m and above, whereas no consistent improvement was observed at lower error levels. In a preliminary comparison with random forests, XGBoost, Long Short-Term Memory (LSTM), and Gated Recurrent Unit models using the same training, validation, and test samples, the Feedforward Neural Network (FNN) achieved competitive test MSE while requiring substantially less training time and runtime memory than the LSTM. These findings support the proof-of-concept feasibility of lightweight FNN-based correction for simulated static GNSS positioning. Future work will focus on validation using real GNSS measurements and extension to dynamic positioning applications. Full article

High-precision urban 3D localization is a critical foundation for vehicular Internet of Things (IoT) applications, yet conventional Global Navigation Satellite System (GNSS)-based localization is vulnerable to signal obstruction, multipath effects, and non-line-of-sight propagation in complex environments. To improve localization reliability, this paper proposes a consistency-guided quantitative–qualitative fusion (CG–QQF) framework with vision-based terrain constraints. The framework integrates heterogeneous quantitative sensors, including an absolute positioning source, inertial measurement unit (IMU), wheel encoders, and a steering angle sensor, for continuous metric state estimation, while a monocular camera provides qualitative terrain-slope information. Rather than treating visual perception as a direct metric observation, the proposed method introduces it as a conditional structural constraint that is activated only when it is consistent with the quantitative estimate, thereby regularizing the localization solution and suppressing vertical drift. Although ultra-wideband (UWB) positioning is adopted as the absolute positioning source in the experimental platform, it serves as a generic positioning module and can be replaced by GNSS-based techniques such as real-time kinematic (RTK) and precise point positioning (PPP). In an indoor scaled proof-of-concept experiment over a controlled four-lap dataset, CG–QQF achieves a 3D RMSE of 0.0575 m and a vertical MAE of 0.0042 m. Its 3D RMSE is approximately 4.0% lower than quantitative sensor fusion (QSF), 37.9% lower than absolute-positioning/inertial fusion (ABS–INS), and 49.9% lower than vision-assisted quantitative fusion (VA–QF). These results demonstrate that consistency-triggered qualitative constraints can complement metric sensor fusion without directly introducing uncertain visual measurements, providing a practical mechanism for improving the robustness and vertical stability of heterogeneous localization systems. Full article

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) archives usually provide only one delay–Doppler map (DDM) for each recorded observation condition, limiting the representation of residual DDM variability. This study proposes a Position-Guided Conditional Normalizing Flow (PGCFlow) for observation-grounded probabilistic expansion of ocean bistatic radar cross section (BRCS) DDMs. PGCFlow uses four invertible affine coupling blocks to map a 17 × 11 DDM to an equal-dimensional Gaussian latent space. Wind–Auxiliary Condition Modulation incorporates a seven-dimensional condition vector into affine-parameter prediction, while Position-Guided Cross-Partition Aggregation (PGCA) uses deterministic grid descriptors to retain explicit cell locations and facilitate spatial-dependence modeling. Experiments used 5,819,042 quality-controlled CYGNSS observations from 2024. PGCFlow was compared with a conditional variational autoencoder and a generic conditional invertible neural network on 8000 held-out recorded conditions drawn from the same empirical observation domain, with 16 generated DDMs per condition. Although the cVAE achieved the highest balanced-aggregate structural similarity (SSIM) of 0.9396, PGCFlow obtained the lowest Fair Energy Score (FES) and Variogram Score (VS) of 0.2242 and 0.0641 and the closest relative local-neighborhood dispersion to unity at 1.0501. It also achieved the lowest frozen-estimator response RMSE and response MAE of 1.1900 and 0.9129 m/s, respectively. Ablation results indicated individual contributions from both proposed modules. Overall, PGCFlow achieved a favorable trade-off among the evaluated fidelity, dependence, dispersion, and response-consistency measures. Full article

UAV visual localization aims to utilize real-time ground observation imagery captured by drone platforms to retrieve the most relevant images from a large-scale satellite remote sensing image database and thereby estimate their corresponding geographic coordinates. It is a critical task in autonomous navigation of unmanned systems, emergency reconnaissance, and low-altitude remote sensing applications. Existing UAV visual localization methods typically rely on global feature similarity to rank candidate satellite tiles and directly adopt the center of the Top-1 tile as the localization result, leading to unstable rankings and coordinate estimation errors in continuous area localization tasks. To address these issues, this paper proposes a token-level local matching reranking method and a neighborhood-consistent position fusion method for UAV visual localization. First, a global search efficiently retrieves a Top-K candidate set from a large-scale reference database. Second, a token-level local matching reranking module is introduced, which utilizes local token interactions, neighborhood geometric priors, and candidate relationship modeling to perform fine-grained reranking and score calibration of high-confidence candidates, thereby enhancing the reliability of the top candidates’ rankings. Finally, a neighborhood-consistent position fusion strategy is proposed, which adaptively fuses and predicts position coordinates by jointly utilizing the spatial distribution and confidence relationships of multiple candidate satellite tiles to mitigate the discretization errors caused by center-based localization using a single tile. Experimental results on the GTA-UAV and UAV-VisLoc datasets demonstrate that the proposed method effectively improves candidate ranking quality and reduces meter-level localization errors under same-area settings. Compared with the Global Retrieval baseline, over five independent runs under the GTA-UAV same-area setting, the proposed method achieves an average Recall@1 (R@1) gain of 2.89 percentage points and reduces the average Dis@1 localization error by 60.95 m. In a single-seed evaluation under the UAV-VisLoc same-area setting, it also improves R@1 by 3.03 percentage points and reduces Dis@1 localization error by 39.53 m. Full article

The Global Navigation Satellite System (GNSS) has been widely adopted in navigation applications due to its high accuracy and convenience. However, in urban canyon environments, severe signal blockage caused by buildings and trees introduces substantial non-line-of-sight errors and multipath effects, leading to degraded and highly fluctuating positioning performance. To address this issue, this paper proposes a residual-guided hybrid stochastic modeling framework. The method operates in two stages: first, a pseudo-range correction estimation network takes GNSS parameters containing residuals as input to estimate pseudo-range correction; second, these estimated corrections together with elevation angle and carrier-to-noise ratio are fed into a hybrid stochastic model parameter estimation network to determine model parameters. This design adjusts the pseudo-range observations to reduce the positioning loss without requiring true pseudo-range errors, which are difficult to obtain in real-world scenarios. Meanwhile, the explicit modeling of relationships among pseudo-range correction, elevation angle, and carrier-to-noise ratio renders the stochastic model parameters interpretable. Experiments on public urban GNSS datasets demonstrate that the proposed method achieves competitive positioning performance against both conventional and learning-based baselines. It delivers notable accuracy improvements in light urban canyon environments, particularly on the KLT2 sequence, while maintaining robust and competitive performance in the more challenging TST and Mong Kok scenarios. These results validate the effectiveness of jointly estimating pseudo-range corrections and adaptive observation weights for enhancing positioning accuracy and robustness across diverse urban environments. Full article

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

The new concept of air traffic control favours the global navigation satellite system (GNSS) Galileo over the classic navigation system. Practice shows that GNSSs are unreliable when operating in the presence of interference. That is why we examined the feasibility of measuring the distance between a Galileo receiver on board a flying object and a Galileo satellite using the E6-C pilot signal. We derived algorithms to measure the distance between the GNSS Galileo receiver and the satellite in the presence of narrowband interference. To derive algorithms for processing the E6-C signal in a global navigation satellite system receiver, we used a quasi-optimal nonlinear filtering method with a quadratic loss function. When creating the simulation models, we set the following conditions: the user receiver can demodulate the primary code of the E6-C signal, the measurement results are not affected by the E6-C transition through the atmosphere and troposphere, and the receiver operates under interference conditions. We have not yet verified the proposed distance measurement method with receiver hardware designed according to our proposed architecture. The simulation results showed that the distance measurement error between the Galileo satellite and the user receiver was +0.16 m and remained approximately the same throughout the simulation period. We verified by simulation, according to the algorithms and the selected positions of the satellites, that if we can measure all four distances of the Galileo user’s receiver from the cooperating satellites with errors that do not exceed 0.16 m, then the maximum errors in determining his X, Y, and Z coordinates would be 0.28 m. Their maximum root mean square (RMS) values were 0.11 m, with the magnitudes of these errors depending on the receiver’s position relative to the satellites. The advantage of this method is the very short convergence time. The disadvantage of the presented algorithms is the need for matrix multiplication, which places high demands on the signal processor’s data processing speed. We assume that this shortcoming can be eliminated by simplifying the algorithms, a direction that warrants further research. Full article