Search Results (1,705)

Reliable high-precision positioning in railway tunnels is essential for intelligent train operation and safety monitoring, yet GNSS signals are severely degraded by blockage and multipath. This paper proposes a deployment-oriented numerical framework to optimize pseudolite layouts in tunnels by explicitly modeling visibility obstruction and controlling worst-case geometry along the train trajectory. A high-fidelity 3D tunnel–train model is established, in which line-of-sight (LoS) availability is screened under vehicle occlusion and trajectory-level geometric quality is evaluated accordingly. Instead of optimizing only the average PDOP, the proposed framework minimizes the trajectory 90th-percentile PDOP (qPDOP) to suppress tail-risk geometric degradation, while interpreting PDOP as an error amplification factor that directly affects positioning reliability under measurement noise and local multipath. The core contribution is a Voronoi-partition-constrained improved genetic algorithm (IGA) for tunnel pseudolite deployment. Voronoi partitioning enforces segment-wise coverage by requiring at least one pseudolite in each partition cell and avoids clustering-induced blind zones. Meanwhile, the IGA incorporates improved search and constraint-handling mechanisms to satisfy practical engineering requirements, including feasible installation regions, minimum spacing, mounting-face balance (ceiling/side walls), communication range, and continuous satellite visibility. Comparative simulations and ablation studies demonstrate that the proposed method achieves more uniform coverage and significantly improves full-trajectory geometric stability, reducing high-quantile PDOP and mitigating local spikes in occlusion-sensitive sections under cost-constrained sparse deployments. The proposed framework provides a practical and flexible toolchain for designing positioning-oriented pseudolite infrastructures in underground transportation environments. Full article

The incorporation of artificial intelligence, multi-sensor perception, and cyber-physical control into mining operations offers tremendous opportunities for increasing productivity, safety, and sustainability. However, present frameworks focus on discrete subsystems rather than providing a unified, safety-constrained optimization method that has been verified in both surface and underground environments. This paper describes a scalable, hierarchical autonomous mining architecture that incorporates sensor fusion, edge intelligence, fleet coordination, and digital twin-based decision support. It is designed to operate in GNSS-denied conditions and extreme climatic constraints common to Nordic mining environments. A mathematical modeling approach formalizes vehicle dynamics, drilling mechanics, and multi-agent fleet coordination inside a safety-constrained multi-objective optimization formulation. The framework is validated using Monte Carlo simulation with uncertainty measurement, sensitivity analysis, and statistical hypothesis testing. The preliminary results show improvements over a typical baseline, with productivity increasing by approximately 24.3% ± 3.2%, energy consumption decreasing by 12.8% ± 2.5%, and safety risk decreasing by 48.6% ± 4.1%. A sensitivity study identifies localization accuracy, communication delay, and optimization weighting as the primary system performance drivers. The suggested framework serves as a reproducible and transferable reference model for next-generation intelligent mining systems, having direct applications to both industrial deployment and future research in autonomous resource extraction. Full article

Soil penetration resistance (Pr) measurement is important for assessing compaction and permeability; however, Pr is heavily dependent on soil moisture. Therefore, the interpretation of Pr data is significantly more reliable if moisture is measured simultaneously and in the same soil layer. In addition, reliable assessment of permeability requires consideration of both soil moisture and penetration resistance. The aim of this work was to develop a prototype of a hand-held combined device in which a volumetric moisture sensor operating on the principle of a galvanic cell is integrated into the Pr measurement cycle, allowing simultaneous measurements at different depths. The device simultaneously determined the penetration resistance acting on the cone, the measurement depth (with a laser sensor), the volumetric moisture (Cu–Zn electrode pair), and the location of the measurement site (GNSS). The moisture sensor was found to be neutral to the influence of the mineral part of the soil on moisture measurement, which in the case of other alternative measurement methods significantly affects the soil moisture measurement data. The calibration of the galvanic moisture sensor was performed under laboratory conditions (VWC 5–50%) based on a gravimetric reference. The relationship was approximately linear at lower moistures and nonlinear at higher moistures. The salinity effect test indicated that the TDR-based reference device gave a strongly overestimated moisture reading in saline soil, while the galvanic cell-based measurement remained within a realistic range compared to the gravimetric method. The results indicate that Pr measurement integrated with a galvanic sensor creates a practical prerequisite for the simultaneous collection of Pr and moisture profiles and is useful in conditions where dielectric methods are affected by salinity or minerality interference. Full article

Spaceborne LiDAR systems, such as ICESat-2, provide critical data for global land cover and topography; however, their performance in rugged, vegetated landscapes requires rigorous local validation. This study evaluates the vertical accuracy of ICESat-2 ATL08 terrain height metrics in the complex Turkish Western Black Sea region, utilizing a reference dataset of high-precision terrestrial GNSS measurements. Following strict IQR-based outlier detection and photon density filtering, 1637 spatially matched segments were analyzed. The h_te_best_fit terrain height metric showed the best agreement with the terrestrial GNSS reference data, yielding an RMSE of 3.37 m and a mean bias of −0.42 m, indicating a slight underestimation of the terrain surface. The univariate analysis revealed a strong positive correlation between terrain slope and vertical error, indicating that slope is the prominent degradation factor contributing to pulse broadening. Additionally, dense forest cover was found to limit ground photon retrieval, leading to increased error margins, whereas nighttime acquisitions offered slightly improved precision. These findings suggest that while ATL08 is a valuable topographic source, slope-dependent corrections are essential for applications in mountainous environments. Full article

Artificial intelligence (AI), cloud computing, robotics, automation, and remote sensing technologies are all contributing to digital transformation in forestry. Improving on low-accuracy Global Navigation Satellite Systems (GNSS) positioning affected by multipath error and interception under forest canopies is critical for integrating smart and digital technologies into equipment in forest operations. In an era where lidar-derived individual tree locations are now increasingly available in digital forest inventories, a possible alternative approach to positioning resources such as people or equipment accurately could be to match locally-measured tree positions and attributes in the forest with an existing global reference map based on prior remote sensing missions, effectively using the trees themselves as satellites to circumvent the need for GNSS-based positioning. We evaluated a lidar-based alternative to GNSS positioning using predicted tree positions from local terrestrial laser scanning (TLS) matched with a global stem map derived from prior airborne laser scanning (ALS), a methodology we refer to as TreePS. The horizontal error of the TreePS system was estimated using 154 permanent single-tree inventory plots on the University of Idaho Experimental Forest with two different workflows based on two common R packages (lidR v. 4.3.0, FORTLS v. 1.6.2) using either spatial coordinates or spatial plus stem DBH predicted using one or both segmentation routines and a custom matching algorithm. Mean TreePS error using lidR for below and above-canopy segmentation had mean error of 1.04 and 2.04 m with 93.5% and 91.6% of plots with viable match solutions on spatial and spatial plus DBH matching. The second workflow with both FORTLS (TLS point cloud) and lidR (ALS point cloud) had errors of 1.09 and 2.67 m but only 57.9% and 54.2% of plots with solutions using spatial and spatial plus DBH, respectively. There is room for improvement in the matching algorithm but the TreePS methodology and similar feature-matching solutions may be useful for below-canopy positioning of equipment, people or other resources under dense forests and other GNSS-degraded environments to help advance smart and digital forestry. Full article

There is increasing interest in space domain awareness (SDA), motivating the use of non-traditional sensors for space surveillance. One such sensor is the Buckland Park Stratospheric–Tropospheric (BPST) very high frequency (VHF) radar, which has demonstrated an ability to detect over 2000 resident space objects (RSO) daily. A by-product of the RSO observations is the measurement of ionospheric group retardation, which can be used to estimate the total electron content (TEC) between the ground and the satellite altitude. This paper describes the use of BPST radar observations of Starlink satellites to measure vertical TEC (vTEC) from the ground to 490 km and from the ground to 560 km. The variation in BPST radar vTEC is demonstrated for both geomagnetically quiet and storm periods. The results are combined with global ionospheric TEC maps to calculate the ratio of the ionospheric to plasmaspheric (or LEO to GPS) vTEC. This allows investigation of the diurnal and annual variation in the LEO to GPS vTEC for the radar location at a temporal resolution unavailable to LEO satellite-based measurements. The results indicate that the RMS uncertainty of the BPST radar vTEC estimates is 0.41 TEC units (TECU), comparing favorably with the ≈2 TECU RMS uncertainty typically measured by GNSS receivers. The technique described in this paper may be applied to any ST or boundary layer (BL) radar without the need for hardware changes. Full article

As a key provider of Positioning, Navigation, and Timing (PNT) information, the characteristics of Global Navigation Satellite System (GNSS) signals, including types, Quality Indicators (QIs), and measurements, should be understood. This study employs temporally correlated deep learning models to classify GNSS signals as Line-of-Sight (LOS) or non-LOS using four QIs: the elevation angle, Carrier to Noise Ratio (C/N₀), code measurement’s standard deviation, and difference in azimuth angle. Autocorrelation analysis confirmed that these QIs exhibit significant temporal dependencies. The Bidirectional LSTM (Bi-LSTM) model, with four hidden layers, 64 units, and a sequence length of 18, achieved the best performance: 94.17% classification accuracy and a 2.61% False Positive (FP) rate. Positioning based on classified LOS signals significantly improved accuracy, reducing the mean errors in the horizontal, vertical, and 3D domain by 36.6%, 81.4%, and 59.6%, respectively, and reducing the Standard Deviation (STDEV) by 46.3%, 33.5%, and 45.5%, respectively. Moreover, the non-LOS probability output enables flexible signal selection and mitigates the issue of insufficient signal availability. These results highlight the effectiveness of temporally correlated models in GNSS signal classification and positioning performance. Full article

Accurate measurement of the spatial position of targets in a fixed camera is critical in remote sensing applications. Visual spatial positioning methods that rely solely on images are susceptible to adverse factors such as inaccurate camera calibration, imprecise image target detection, and incorrect feature point selection. Complementary to images, the ubiquitous Global Navigation Satellite System (GNSS) data can provide spatial positions of targets, but most of them are low-cost GNSSs with significant positioning noise. In order to fuse these two valuable but flawed positioning measurements to improve the accuracy and stability of spatial positioning, we propose a deep learning multi-modal spatial positioning method by fusing sequential uncalibrated video images and low-cost GNSSs. Firstly, a self-supervised cascade denoising auto-encoder (SCDAE) architecture is built to endow the auto-encoder with robustness to noise in the raw inputs. Then, based on the SCDAE and Bayesian optimal estimation, a Bayesian self-supervised multi-modal fusion positioning method SCDAE-MFP is presented to achieve accurate and stable spatial positioning by self-supervised manifold learning. Specifically, to provide visual self-supervision to the SCDAE-MFP, a visual position denoising auto-encoder module based on dual unsupervised learning is proposed. Extensive experimental results on public datasets showed that SCDAE-MFP outperformed five other classical and state-of-the-art baseline methods by an average of 56.79% in reducing positioning errors. Full article

Upper troposphere (UT) humidity records are crucial for climate studies. To maximize temporal representativeness and enhance the lidar signal, pseudo-monthly averaging—limited to nighttime measurement—is applied, yielding water vapor mixing ratio (WVMR) profiles up to 16 km. This study evaluates 11 years (2013–2023) of WVMR profiles from a UV Raman lidar (Li1200) at Réunion Island, comparing them with MLS-Aura satellite retrievals, ERA5 reanalysis data, and GRUAN-processed M10 radiosondes. The results reveal a systematic dry shift in MLS of up to 30% above 12 km, particularly during the wet season. The lidar exhibits a slight downward shift in WVMR, approximately 5% lower than ERA5 throughout the UT, with the largest deviations occurring above 14 km and greater variability during the wet season. Calibration-related challenges during the dry season result in lidar WVMR profiles that are up to 10% drier than ERA5. Additionally, comparisons with GRUAN-processed radiosondes show a substantial dry shift relative to the lidar, exceeding 30% above 12 km. We investigate the effect of GNSS-based lidar calibration by applying an alternative calibration method, which produces higher WVMR values. This reveals a dry shift in ERA5 relative to the lidar, increasing with altitude in the UT up to 25%. These measurements contribute to the global effort to monitor and validate tropical and subtropical upper tropospheric humidity. Full article

Inertial measurement units (IMUs) in low-cost navigation systems suffer from significant drift and noise errors due to sensor biases, scale factor instability, and nonlinear stochastic noise. This paper proposes a hybrid error compensation approach that combines Fast Orthogonal Search (FOS), a nonlinear system identification technique, with deep Long Short-Term Memory (LSTM) neural networks to improve IMU signal accuracy in GNSS-denied navigation. The FOS algorithm efficiently models deterministic error patterns (such as bias drift and scale factor errors) using a small training dataset, while the LSTM learns the IMU’s complex time-dependent error dynamics from much longer training data. In the proposed method, FOS is first used to predict the output of a high-end IMU based on that of a low-end IMU, and the trained FOS model is then used to extend the training data for an LSTM-based predictor. We demonstrate the efficacy of this FOS–LSTM hybrid on real vehicular IMU data by training with a limited segment of high-precision reference measurements and testing on extended operation periods. The hybrid model achieves high predictive accuracy for predicting the high-end signal based on the low-end signal, with a mean squared error below 0.1% and yields more stable velocity estimates than models using FOS or LSTM alone. Although long-term position drift is not fully eliminated, the proposed method significantly reduces short-term uncertainty in the inertial solution. These results highlight a promising synergy between model-based system identification and data-driven learning for sensor error calibration in navigation systems. Key contributions include FOS-based pseudo-label bootstrapping for data-efficient LSTM training and a navigation-level evaluation illustrating how signal correction impacts dead reckoning drift. Full article

In intelligent vehicle navigation, efficient sensor data processing and accurate system stabilization is critical to maintain robust performance, especially when GNSS signals are unavailable or unreliable. Classical calibration methods for Inertial Measurement Units (IMUs), such as discrete and system-level calibration, fail to capture time-varying, non-linear, and non-Gaussian noise characteristics. Likewise, Kalman filters typically assume static measurement noise levels for non-holonomic constraints (NHCs), resulting in suboptimal performance in dynamic environments. Furthermore, zero-velocity detection plays a vital role in preventing error accumulation by enabling reliable zero-velocity updates during motion stops, but classical thresholding approaches often lack robustness and precision. To address these limitations, we propose a novel multitask deep neural network (MTDNN) architecture that jointly learns IMU calibration, adaptive noise level estimation for NHC, and zero-velocity detection solely from raw IMU data. This shared-encoder design is utilized to minimize computational overhead, enabling real-time deployment on resource-constrained platforms such as Raspberry Pi. The model is trained using post-processed GNSS-RTK ground truth trajectories obtained from both a proprietary dataset and the publicly available 4Seasons dataset. Experimental results confirm the proposed system’s superior accuracy, efficiency, and real-time capability in GNSS-denied conditions. Full article

High-precision and reliable self-localization is essential for autonomous navigation systems. However, in urban canyons (urban environments with clusters of high-rise buildings), Global Navigation Satellite Systems (GNSS) suffer from severe multipath and Non-Line-of-Sight (NLOS) signal reception. This causes a theoretically unbounded positive bias in pseudorange measurements, significantly degrading positioning integrity. To address this challenge, this study proposes a novel GNSS/Inertial Measurement Unit (IMU) tightly coupled integrated navigation system using factor graph optimization (FGO) integrated with machine learning-based NLOS detection. To train the NLOS detection model, we utilized a dual-polarized antenna to label signals based on the strength difference between RHCP and LHCP components, achieving a detection accuracy of 0.89. A random forest classifier identifies NLOS signals, and based on its classification labels, the variance of the corresponding GNSS pseudorange factors within the FGO framework is dynamically inflated. This effectively mitigates the impact of outliers while preserving the graph topology. Experimental evaluations in dense urban environments demonstrated that the proposed method improves horizontal positioning accuracy by 84.8% compared to conventional standalone GNSS positioning. The dynamic integration of machine learning-based signal classification and tightly coupled FGO provides an extremely robust positioning solution, proven to meet the stringent reliability requirements demanded of autonomous systems even under severe signal obscuration. Full article

Train track occupancy detection is essential for railway operation safety and dispatching, yet GNSS-based positioning and track matching can degrade or fail in turnouts and station yards due to multipath, interference, and dense track layouts. This paper presents an IMU-only method to discriminate track-switching events during turnout passage by exploiting the transient change in heading angular velocity. The Z-axis gyroscope measurement (approximately aligned with the track-plane normal) is used as a heading-rate proxy, and a lightweight indicator is constructed from the difference between a short-window moving average and the full-run mean. The full-run mean further serves as an in situ approximation of the gyroscope zero bias, alleviating the need for pre-calibration and improving robustness to systematic drift. A fixed discrimination threshold is determined from stationary gyroscope noise statistics, and the minimum effective operating speed is derived by combining gyro noise characteristics with the kinematic relationship among train speed, turnout curvature radius, and heading rate. Field experiments conducted from January to April 2025 on three railway sections covering 27 turnouts (300 turnout-passage events) show that, using a constant threshold $T_0=0.002\mathrm{rad}/\mathrm{s}$, the proposed method achieves 100% track-switching discrimination accuracy within 5–40 km/h, without requiring track maps, GNSS, or prior databases. Full article

Geomagnetic navigation is a promising alternative for positioning and localization of UAV swarm systems in GNSS-denied environments. However, strong and heterogeneous electromagnetic interference generated by onboard power, propulsion, and electronic subsystems severely degrades magnetic measurement fidelity, limiting the achievable accuracy of cooperative UAV swarm navigation. To address this challenge, this paper proposes PG-TLNet, a physics-guided aeromagnetic interference compensation framework based on the extended Tolles–Lawson (T–L) model. By integrating onboard state information (current, voltage, and attitude) with magnetic measurements through physics-consistency constraints and a lightweight multi-branch convolutional neural network, the framework enables robust real-time compensation under strong and time-varying interference while remaining suitable for resource-constrained UAV nodes. Experimental validation using multiple scalar magnetometers under heterogeneous interference conditions, with amplitudes up to 1000 nT, shows that PG-TLNet consistently outperforms the conventional T–L model across all sensing nodes, maintaining residual magnetic interference at approximately 0–30 nT under long-duration and highly dynamic operations. The proposed method achieves an improvement ratio (IR) of up to 15 with an end-to-end inference latency below 94 μs. These results indicate that PG-TLNet meets the practical measurement fidelity requirements for geomagnetic navigation in GNSS-denied environments. By ensuring reliable and consistent magnetic measurements at the individual UAV node level, the proposed framework establishes a practical sensing foundation for geomagnetic navigation and distributed magnetic sensing in UAV swarm systems operating in GNSS-denied environments. Full article

Dam deformation monitoring is essential for ensuring the safe operation of hydraulic structures, yet practical data are often compromised by missing values and noise, while spatial coupling among monitoring points further complicates prediction. To address these challenges, this study proposes a Spatio-Temporal Multi-Graph Attention Fusion Network (STMGAFN) for dam deformation prediction and risk early warning under incomplete data conditions. Data quality is enhanced through a data-quality-aware hierarchical adaptive imputation mechanism combined with a VMD–wavelet joint denoising strategy. A multi-graph spatial modeling framework integrating temporal similarity, spatial proximity, structural zoning, and measuring-line connectivity is constructed, and fuses multi-source spatial features through a lightweight adaptive attention mechanism. A parameter-sharing recursive probabilistic temporal modeling approach is adopted to jointly predict deformation values and their associated uncertainties. Based on the predicted confidence intervals, a four-level risk classification and early-warning scheme is established. Experimental results on real GNSS monitoring data from dam sites demonstrate that the proposed method achieves an RMSE of 0.3588 mm, an MAE of 0.1738 mm, and an R² of 0.9865, outperforming baseline models including LSTM, TCN, CNN-LSTM, and STGCN. Moreover, the correlation between predictive uncertainty and actual error reaches 0.892, verifying the effectiveness and reliability of the proposed method for dam safety monitoring under complex conditions. Full article