Search Results (357)

Energy-efficient and resilient decentralized unmanned aerial vehicles (UAV) swarm operation in global navigation satellite system (GNSS) denied environments remains challenging because propulsion demand, communication load, and onboard inference are tightly coupled at the mission level. Although prior studies have examined some of these components separately, their joint evaluation within adaptive decentralized swarms remains limited under degraded navigation conditions. This study proposes an energy-aware adaptive communication-topology framework integrated with lightweight edge artificial intelligence (AI)-assisted navigation for decentralized UAV swarms operating without reliable GNSS support. The approach combines a unified mission-level energy-accounting structure for propulsion, communication, and onboard inference, a residual-energy-aware topology adaptation mechanism for preserving swarm connectivity, and a convolutional neural network-long short-term memory (CNN–LSTM) based edge-AI navigation module for improving localization robustness. The framework was evaluated in 1200 s Robot Operating System 2 (ROS2)–Gazebo–PX4 simulation scenarios against fixed topology and extended Kalman filter (EKF)-based baselines. Under the adopted simulation assumptions, the proposed configuration achieved a 22.7% reduction in total energy consumption, with the largest decrease observed in the communication-energy component, while preserving positive algebraic connectivity across all evaluated runs. The edge-AI module yielded a 4.8% root mean square error (RMSE) reduction relative to the EKF baseline, indicating a modest but meaningful improvement in localization performance. These results support the feasibility of integrated energy-aware swarm coordination in GNSS-denied environments; however, they should be interpreted as simulation-based evidence under the adopted modeling assumptions, and further high-fidelity propagation modeling, broader learning validation, and hardware-in-the-loop studies remain necessary. Full article

Paddy field water management and rice phenology strongly affect crop productivity and environmental processes, requiring continuous and quantitative monitoring. This study combined satellite synthetic aperture radar (SAR) observations and ground-based Global Navigation Satellite System (GNSS) interferometric reflectometry (GNSS-IR) over a paddy field to analyze their sensitivities to water-level variations and phenological dynamics. Sentinel-1 (C-band) and ALOS-2/PALSAR-2 (L-band) SAR time series were compared with continuous GNSS-IR observations acquired using geodetic-grade instrumentation. For GNSS-IR, Lomb–Scargle periodogram (LSP) analysis of SNR data was applied to derive two indicators: (i) the dominant spectral peak (f_water) frequency associated with the effective reflecting surface, and (ii) a normalized spectral integral (GNSS Phenology Indicator, GPI) representing vegetation-induced scattering and attenuation effects. The temporal evolution of LSP spectra exhibited systematic changes with rice phenological progression, including peak broadening and the emergence of multiple peaks as vegetation developed. For water level variations, L-band SAR co-polarized backscatter (VV and HH) and the GNSS-IR spectral peak exhibited comparable relationships with in situ water level, whereas C-band SAR showed weaker sensitivity. For phenological dynamics, GPI showed temporal behavior similar to that of the SAR polarization ratio (VH/VV), with clear responses around key growth stages, such as heading and harvest. These results suggest that SAR polarization-based indicators and GNSS-IR spectral characteristics can be interpreted within a consistent electromagnetic framework: co-polarized L-band SAR responses correspond to the water-surface-related GNSS-IR peak, whereas cross-polarized indicators correspond to GPI. This study demonstrated the potential of GNSS-IR as complementary information for physically interpreting SAR scattering mechanisms, highlighting a pathway toward more integrated microwave-based monitoring of land surface processes. Full article

Inland water bodies are vital to the Earth’s ecosystem, global water cycles, and climate regulation. Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a powerful tool for water detection, particularly with the deployment of the Fengyun-3 (FY-3) E, F, and G satellites. This study proposes an inland water body detection method by integrating the Z-score algorithm with specular point land surface reflectivity (SR_sp) derived from FY-3 Level-1 GNSS-R data. Using 2024 observations, the method was validated in the Amazon and Congo basins against optical water body products. The results demonstrate high detection performance, achieving overall accuracies of 95.39% and 97.38% in the two regions, respectively. Analysis of reflectivity expressed in decibels (dB) reveals that while dB-units enhance the detection of small tributaries, they are more susceptible to noise-induced misclassification compared to linear units. Furthermore, a comparative assessment of GNSS constellations shows that multi-system combination significantly reduces noise compared to single-system approaches. Notably, the Galileo system exhibited limited sensitivity to small tributaries due to lower observational density. Sensitivity analyses further reveal that interpolation methods and Z-score threshold selection are important factors influencing detection accuracy. As the first systematic evaluation of FY-3 GNSS-R data for inland water detection, this research provides a critical benchmark for future multi-platform and multi-constellation land surface retrieval studies. Full article

Detecting non-line-of-sight (NLOS) signals is essential for improving the accuracy and reliability of smartphone Global Navigation Satellite System (GNSS) positioning in dense urban areas. This paper presents a practical method for NLOS detection based on skymasks derived from smartphone observations. The observable rates of satellite observation series are first computed using precise ephemeris, and the observations are then classified into blocked and unblocked groups. A smoothing spline is then applied to fit the building boundary from the categorized series. Based on the fitted boundary, a skymask is constructed and used for NLOS detection. Datasets collected at three locations using three different smartphones are used for validation. The results show that both the number and proportion of NLOS signals decrease significantly after applying the proposed method. As the degree of obscuration increases, the detection accuracy remains stable across different smartphones. In some cases, single-point positioning accuracy is improved after excluding NLOS signals. In addition, the derived skymask can be used to estimate sky visibility and support the selection of positioning strategies. Overall, the proposed method can be combined with the consistency checking method for NLOS detection, as it does not require additional information. Full article

Accurately characterizing the kinematics of slow-moving urban landslides remains a major scientific and operational challenge, because no single monitoring technique can simultaneously provide spatially continuous deformation patterns and reliable three-dimensional displacement measurements. This study investigates the spatial and temporal evolution of a slow-moving landslide affecting the University of Azuay campus in Cuenca (Ecuador), where ongoing ground deformation has caused structural damage to several buildings. An integrated monitoring strategy combining GNSS measurements, Sentinel-1 multi-temporal DInSAR analysis, and geophysical investigations (ERT and seismic profiling) was adopted to characterize landslide kinematics and constrain subsurface conditions. GNSS observations revealed that the north–south displacement component was dominant, with cumulative displacements exceeding 20 cm during the monitoring period (from July 2021 to June 2024), while east–west displacements were on the order of 10 cm. MT-DInSAR analysis delineated the spatial extent of the unstable area and identified mean deformation rates of up to approximately −1.5 cm/year in the central sector of the landslide. The combined interpretation of geodetic and geophysical data indicates that slope instability is controlled by saturated fine-grained layers and mechanical contrasts, with the basal sliding zone associated with weak levels of the Mangan Formation. Overall, the results demonstrate the value of a multi-sensor, component-wise monitoring strategy for improving the reliability of deformation estimates and for supporting landslide risk assessment and land-use planning in complex urban environments. Full article

Global Positioning System (GPS)-derived ionospheric electron concentration measurements provide a powerful observational framework for seismo-electromagnetic studies, enabling quantitative investigation of lithosphere–atmosphere–ionosphere coupling processes through statistically detectable perturbations in ionospheric electron concentration. We analyze GPS-derived Vertical Total Electron Content (VTEC) variations associated with the 14 August 2021 Haiti earthquake ($M_w$ 7.2) and the 11 January 2022 Cyprus earthquake ($M_w$ 6.6) using data from nearby International GNSS (Global Navigation Satellite System) Service (IGS) stations located within their respective earthquake preparation zones. VTEC time series spanning 45 days before and 7 days after each event are processed to remove the diurnal component, yielding residuals that isolate short-term ionospheric variability. Anomaly detection is performed using three statistical frameworks: a Gaussian mean, standard deviation model, a robust median/median absolute deviation (MAD) model, and a distribution-free quantile-based model. Daily “occurrence” and “energy” indices are constructed to quantify the frequency and cumulative strength of detected anomalies, respectively. While the indices exhibit similar temporal patterns across all methods, they indicate frequent anomaly detection, limiting statistical selectivity. To address this, both indices are normalized by their median values and filtered using a 95% quantile threshold, retaining only extreme deviations. This procedure substantially reduces background fluctuations and isolates a small number of statistically significant anomaly peaks. For both earthquakes, enhanced anomaly activity is identified in the weeks preceding the events, whereas post-event peaks coincide with periods of elevated meteorological and geomagnetic activity. The results demonstrate that normalization combined with robust statistical methods is essential for discriminating significant ionospheric TEC anomalies from background variability. Full article

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) provides a low-cost, all-weather approach for continuous soil moisture content (SMC) retrieval. However, in single-constellation, multi-satellite applications, the optimal satellite number and the combined effects of multiple environmental factors on retrieval accuracy and stability remain insufficiently quantified. To address these issues, this study develops a dual-frequency GNSS-IR SMC retrieval framework that explicitly incorporates multiple environmental factors. Entropy-based fusion (EFM) is used to adaptively weight dual-frequency phase-delay observations, and a marginal-gain criterion is introduced to determine a suitable number of participating satellites. On this basis, univariate linear regression (ULR) and random forest (RF) models are established, and the Normalized Difference Vegetation Index (NDVI), temperature, and precipitation are incorporated into the RF model to improve retrieval robustness and quantify the relative contributions of environmental factors. The results show that multi-satellite combinations significantly improve SMC retrieval performance, while the incremental gain exhibits clearly diminishing returns and converges when the number of participating satellites reaches about 5–6 within a single constellation. Dual-frequency fusion consistently outperforms single-frequency schemes across different GNSS constellations, demonstrating the complementary value of multi-frequency information under multi-satellite conditions. In addition, the environmentally informed nonlinear model achieves higher accuracy and stability than the linear model, and the dominant environmental drivers differ across stations. Overall, this study provides quantitative support for configuring single-constellation multi-satellite GNSS-IR soil moisture monitoring schemes and for improving retrieval robustness under complex environmental conditions. Full article

Accurate and continuous outdoor pedestrian positioning using smartphones remains challenging in complex environments like urban canyons, where Global Navigation Satellite System (GNSS) signals are frequently degraded or blocked, and Pedestrian Dead Reckoning (PDR) suffers from cumulative errors. To address this, this paper proposes a novel fusion method based on a Robust Adaptive Cubature Kalman Filter (RACKF). The core of our approach is a two-stage filtering architecture: the first stage employs a quaternion-based RACKF to optimally fuse gyroscope and magnetometer data for robust heading estimation; the second stage performs the core fusion of GNSS observations with an enhanced 3D PDR solution. Key innovations include an adaptive noise estimation strategy combining fading and limited memory weighting, a robust M-estimator-based mechanism to suppress outliers, and the integration of differential barometric height measurements. Experimental results demonstrate that the proposed method achieves a horizontal positioning accuracy of 3.28 m (RMSE), outperforming standalone GNSS and improving 3D PDR by 25.97% and 10.39%, respectively. This work provides a practical, infrastructure-free solution for robust smartphone-based outdoor navigation. 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

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

In complex maritime environments and scenarios with severe signal interference, unmanned surface vehicle (USV) swarms face dual challenges: unreliable GNSS signals due to interference and difficulties in accurately calibrating multi-sensor installation errors. These issues severely constrain the capability for high-precision cooperative formation operations. To address these problems, this paper proposes a cooperative localization and all-source online calibration algorithm based on a unified factor graph optimization framework. First, a tightly coupled all-source graph framework is established, integrating navigation radar, electro-optical systems (EOSs) with laser rangefinders, IMU, and GNSS into a sliding window. By leveraging high-precision mutual observations among the swarm, strong geometric constraints are constructed to mitigate the drift of individual inertial navigation systems. Second, an adaptive GNSS weighting mechanism based on signal quality and a degradation detection strategy based on eigenvalue analysis of the Fisher Information Matrix (FIM) are designed. These mechanisms enable online identification and robust estimation of extrinsic parameters, effectively resolving calibration divergence under weak excitation conditions such as straight-line sailing. Finally, the proposed algorithm is validated using field data from three USVs combined with simulated interference experiments. Results demonstrate that the algorithm can rapidly converge to high-precision calibration parameters without artificial targets (radar translation error < 0.2 m, EOS rotation error < 0.05°). During periods of simulated GNSS interference, the cooperative localization root mean square error (RMSE) is reduced to 2.85 m, representing an accuracy improvement of approximately 84.5% compared to traditional methods. This study achieves a “more accurate as it runs” cooperative navigation effect, providing reliable technical support for USV swarm applications in GNSS-denied environments. Full article

In Global Navigation Satellite Systems (GNSS), high-precision position coordinates are typically determined by establishing a double-difference carrier phase observation model and resolving the integer ambiguities within it. Therefore, the ability to fix integer ambiguities rapidly and accurately is a critical challenge in carrier phase measurements. To address the problem of double-difference integer ambiguity, this paper proposes a Hybrid Adaptive Differential Evolution Grey Wolf Optimizer (HADE-GWO) algorithm. Comparative experiments focusing on computation speed and stability were conducted against the GWO, LAMBDA, and M-LAMBDA algorithms. The results show that while achieving the same fixing success rate as the LAMBDA and M-LAMBDA algorithms, the HADE-GWO algorithm finds the optimal ambiguity solution in less time. To validate the high-dimensional ambiguity resolution capability of the HADE-GWO algorithm, 6-dimensional and 12-dimensional integer ambiguity resolution tests were performed. The outcomes indicate that the HADE-GWO algorithm possesses excellent high-dimensional resolution capabilities. Finally, an application experiment was conducted using single-frequency data from GPS and BeiDou (BDS) systems. The results demonstrate that the algorithm can achieve centimeter-level positioning accuracy in a combined single-frequency GPS+BDS solution. Full article

Coal mining in plain regions and its related surface subsidence and geological hazards have been extensively studied, whereas research on mining-induced hazards in mountainous areas remains limited. This knowledge gap has contributed to the frequent occurrence of mining disasters, particularly under steep ridge-type mountain geometry, where deformation characteristics, large-scale slope failure risks, and mining-induced hazard mechanisms remain poorly understood. In this study, a mining area in Zhenxiong, Zhaotong, Yunnan Province, China, is investigated using SBAS-InSAR, GNSS observations, UAV surveys, optical satellite imagery, and detailed field investigations. Surface hazards triggered by coal extraction are identified, and the response relationship between surface subsidence and mining activities is analyzed to reveal the development mechanisms of surface deformation beneath steep ridge-type mountain geometry. The results show that: (1) deep coal mining can still induce significant surface deformation due to the combined amplification effects of steep slopes and lithological conditions; (2) mining-induced deformation does not necessarily evolve into large-scale slope collapse and may gradually stabilize through natural adjustment processes; (3) SBAS-InSAR, validated by GNSS and field observations, provides an effective approach for detecting mining-related subsidence; (4) surface deformation in the study area is jointly influenced by multiple working faces; and (5) strong coupling between the unique steep ridge-type mountain geometry and underlying coal extraction leads to a compound disaster chain under multi-source interactions. These findings offer a critical scientific understanding of mining-induced deformation beneath steep ridge-type mountain geometry and provide important guidance for geological hazard prevention and control in similar mountainous mining areas. Full article

Pseudolite (PL) positioning technology is one of the effective methods to achieve high-precision indoor positioning. The Distributed Array Pseudolite System (DAPLS) is a ground-based augmentation architecture designed to provide high-precision positioning in GNSS-denied or indoor environments. However, maintaining the stability and integrity of pseudolite signals in distributed deployments remains a significant challenge. To address this, a Pseudolite Monitoring Station (PMS) was developed for real-time signal observation, performance evaluation, and anomaly detection. The proposed PMS integrates a multi-channel front-end, signal-processing engine, and monitoring algorithms capable of continuous assessment across three hierarchical levels: Signal Quality Monitoring (SQM), Receiver Processing Monitoring (RPM), and Measurement Quality Monitoring (MQM). To integrate multi-domain monitoring results, a Composite Quality Index (CQI) model is introduced, combining normalized sub-scores through weighted fusion to reflect overall system integrity. A comprehensive Signal Quality Assessment (SQA) framework is further introduced, including four dimensions of evaluation: constellation status, time reference, spatial coordinate reference, and signal anomaly detection. An indoor DAPLS experiment was conducted within a laboratory-level test field. The system comprised three pseudolite transmitter arrays (six transmitters each) and a central monitoring station. Experimental results showed stable synchronization within ±5 ns, coordinate accuracy within 0.2 m, and consistently high signal quality. The monitoring station effectively detected minor signal distortions and synchronization deviations, confirming its diagnostic precision and robustness. This study demonstrates a complete monitoring and evaluation framework for DAPLS, enabling both system-level quality assurance and signal integrity monitoring. The proposed PMS and SQA methods provide essential tools for future deployment of pseudolite-based indoor positioning and timing systems. Full article

Global Navigation Satellite System Reflectometry (GNSS-R) provides a vital means for sea surface wind speed retrieval, yet its application under extreme typhoon conditions remains challenging. Conventional geophysical models (GMFs) saturate in high wind speed regimes (>20 m/s), and deep learning models (e.g., CNNs) are constrained by data sparsity and feature complexity in typhoon environments. To address these issues, we propose a Comparative Learning method of CNN-Transformer with GMF fusion (CLCTG). The CNN branch extracts local coupling patterns, the Transformer branch models global dependencies, and Kullback–Leibler (KL) divergence loss is used for contrastive learning to heighten sensitivity to complex typhoon wind fields. The GMF branch serves as a physical reference/anchor in the low- to moderate-wind-speed range (<20 m/s) to guide the learning of data-driven branches and avoid overfitting by any single data-driven path. The adaptive fusion branch dynamically reweights the three branch outputs, combining local statistical characteristics to improve performance over approximately 0–30 m/s and extending the range of reliable GNSS-R retrieval from about 20 m/s to about 30 m/s; it should be noted that CLCTG exhibits a performance bottleneck in the extreme >30 m/s range. To further improve high-wind-speed predictions, we introduce environmental features based on their correlation with wind speed; ablation experiments demonstrate that the combined use of environmental parameters and CYGNSS features maximizes overall accuracy. Testing on five typhoons from the Eastern and Western Hemispheres confirms CLCTG’s generalization across diverse geographic contexts, and branch-wise comparisons validate its structural advantages. Buoy observations show peripheral errors below 3 m/s and physically consistent wind speed gradients in the core region. These results indicate that multi-source fusion of CYGNSS and environmental data, coupled with contrastive learning and physical reference, offers a reliable and efficient solution for typhoon wind speed retrieval. Full article