Search Results (3,777)

In this article, machine learning (ML) is proposed as a solution to detect and classify false message injection attacks in autonomous ground navigation. First, multiple trajectories are designed and simulated to collect authentic feature samples offered by the odometry and inertial measurement unit (IMU) of an autonomous ground vehicle (UGV). Then, a dataset comprising these samples and other injected samples that simulate two cyberattacks, namely path modification (PM) and velocity drift (VD), is created to train, validate, and benchmark various ML classification models. These include decision tree (DT), k-nearest neighbors (KNN), multi-layer perceptron (MLP), random forest (RF), and support vector machine (SVM). The optimum classification model is experimentally evaluated using a UGV platform, and results suggest that the proposed solution allows the detection of authentic and attacked messages with more than 98% average accuracy and sub-millisecond prediction time. Thus, this solution is ideal for real-time classification, especially in fixed-route applications, e.g., public transportation. Full article

Light detection and ranging (LiDAR) systems are pivotal for precise distance and velocity measurement, yet widespread deployment requires solutions that balance their performance, robustness, and simplicity. Here, we propose a novel chaotic LiDAR system based on a semiconductor vertical-cavity surface-emitting laser (VCSEL) with delayed orthogonal polarization feedback. By exploiting the intrinsic competition between the transverse electric (TE) and transverse magnetic (TM) modes, the system generates polarization-multiplexed dynamics: a chaotic TM mode serves as the reference, while a feedback-modulated TE mode probes the target. This all-in-one source eliminates the need for external optical modulators or complex coherent detection. The system’s dynamics are finely tunable via a half-wave ($\lambda$/2) plate in the feedback loop and the laser injection current, enabling real-time optimization of the cross-correlation signal-to-noise ratio. Experimental results demonstrate precise linear ranging with a resolution of approximately 1.2 cm. Furthermore, the system exhibits strong inherent resistance to external optical interference, maintaining accurate ranging even in the presence of a secondary laser source. This compact, tunable, and interference-resilient platform offers a promising pathway toward low-cost, high-performance LiDAR for applications in autonomous navigation, robotics, and industrial metrology. Full article

Background/Objectives: Cervical pedicle screw (CPS) placement provides superior biomechanical stability but remains technically demanding and associated with a risk of screw malposition. While recent advances in imaging and navigation have improved placement accuracy, reliable prediction of malposition remains challenging. The aim of this study was to evaluate whether machine learning (ML) models can predict CPS malposition using structured clinical and anatomical features. Methods: We performed a retrospective analysis of 862 pedicle screws from 168 posterior cervical spine surgeries conducted at our institution between 2018 and 2025. Clinical, procedural, and anatomical variables, including age, sex, body size parameters, surgical indication, vertebral level, pedicle angle, and pedicle width, were evaluated. Pedicle morphology was partially derived from CT-based automated segmentation using TotalSegmentator (v2.13.0), while selected anatomical parameters were manually measured. Supervised ML models, including Random Forest, Balanced Random Forest, XGBoost (v3.2.0), Support Vector Machine, and K-Nearest Neighbor, were trained and compared using Python and scikit-learn to predict inaccurate screw placement. Model performance was evaluated using Area Under the Receiver Operating Characteristic Curve (ROC AUC), F1-score, precision, and recall. Model interpretability was assessed using Shapley Additive Explanations (SHAP). Results: The dataset showed a clinically representative class distribution, with 91.1% of screws classified as acceptable and 8.9% as inaccurate. Across all models, predictive performance was moderate and consistent. Balanced Random Forest achieved the highest discriminative performance (ROC AUC 0.69) and provided the most balanced classification profile, while other models demonstrated comparable overall performance with varying sensitivity to the minority class. SHAP analysis identified anatomical and procedural variables, including pedicle width and angle, as relevant contributors to model output. Feature contributions were distributed across variables, with substantial overlap between outcome groups. Conclusions: ML-based prediction of CPS malposition using clinical and anatomical features demonstrates consistent and interpretable performance. The results highlight that predictive performance is primarily influenced by dataset characteristics, including class distribution and feature overlap, rather than model selection alone. This study provides an important baseline for ML-based CPS prediction and supports future research integrating larger datasets and more detailed anatomical representations to enhance predictive accuracy. Full article

A cooperative guidance law based on the experience-guided multi-agent proximal policy optimization (E-MAPPO) algorithm is proposed for multiple unmanned aerial vehicles (UAVs) to track dynamic points of interest in civilian applications, such as collaborative search and rescue and environmental monitoring. In multi-UAV cooperative tracking, accurate arrival-time coordination is important for improving collaborative task execution, but it remains challenging because of continuous action spaces, target maneuvering, uncertain time-to-go estimation, and inefficient exploration in multi-agent reinforcement learning. Specifically, a multi-UAV cooperative guidance environment is formulated, and the problem is modeled as a Markov decision process. To address the challenges of large action spaces and poor convergence in multi-agent reinforcement learning, an experience-guided MAPPO framework is introduced to enhance training efficiency and policy stability. Different from standard MAPPO, the proposed E-MAPPO introduces proportional-navigation-guided experience only during the early training stage to guide exploration, while the final policy is still optimized through the MAPPO objective. Subsequently, a composite reward function is designed by integrating distance-based heuristic terms with auxiliary guidance signals, thereby improving exploration efficiency and facilitating coordinated rendezvous and tracking of dynamic references. Comparative simulations with cooperative proportional navigation guidance (CPNG), sliding mode control (SMC), and standard MAPPO are conducted under different target motion scenarios. The results show that E-MAPPO reduces the average convergence step by 17.07% compared with MAPPO. In the straight-moving target scenario, E-MAPPO reduces the cooperative time error by 55.10% compared with CPNG and by 8.33% compared with MAPPO. In the S-type maneuvering target scenario, E-MAPPO reduces the cooperative time error by 55.81% compared with CPNG and by 9.52% compared with MAPPO. Monte Carlo experiments further verify its effectiveness and robustness. Additional robustness tests under Gaussian measurement noise, observation bias, and communication delay show that the proposed method maintains acceptable tracking accuracy and cooperative timing performance under different uncertainty conditions. In addition, the results indicate that the proposed method generalizes well to different types of maneuvering targets. Full article

To address the reduction in nozzle row-following accuracy caused by sensing–execution latency during corn row-following operations, a delay compensation method based on machine vision and dynamic region of interest (ROI) adjustment was proposed. The method integrates real-time forward-velocity information from a global navigation satellite system/inertial measurement unit (GNSS/IMU), decomposes the delays associated with image processing, command transmission, and actuator motion, and calculates a visual look-ahead distance from the total response delay and robot forward velocity. Inverse-perspective mapping was used to establish the relationship between pixel and world coordinates, and the ROI position was dynamically shifted to synchronize the sensing–execution process. Indoor bench tests showed that, under variable conveyor-belt speeds ranging from 0 to 0.25 m/s, the algorithm achieved a row-following accuracy of 93.75% and a lateral mean absolute error of 0.019 m; compared with the average result of the three fixed-ROI tests, the lateral mean absolute error was reduced by 24.8%. Whole-machine tests showed that, under random platform forward speeds of 0–1.00 m/s, the row-following accuracy remained above 85.71%, with a lateral mean absolute error of 0.034 m. The results indicate that the proposed method effectively compensates for system delay under different speed conditions and reduces lateral tracking errors caused by longitudinal spatiotemporal mismatch, providing technical support for the development of precision corn row-following spraying equipment. Full article

Mining-induced subsidence represents one of the most significant long-term geomechanical hazards associated with underground coal exploitation, often continuing for decades after mining activities have ceased. Understanding the persistence and spatial distribution of post-mining ground deformation is essential for evaluating residual geological hazards and improving long-term monitoring strategies in former mining regions. This study investigates the long-term evolution of mining-induced subsidence in the Maleia sector of the Jiu Valley Coal Basin (Romania), an area historically affected by intensive underground coal extraction. A geodetic monitoring network consisting of seventeen permanent benchmarks, initially established in 2006, was reoccupied and remeasured using Global Navigation Satellite System (GNSS) technology in 2026. The comparative analysis was performed against historical measurements acquired during the 2007 monitoring campaign, providing a nineteen-year temporal framework for deformation assessment. Analysis of vertical displacements revealed persistent subsidence at all monitored benchmarks, confirming the continued post-mining adjustment of the geological structure. Measured cumulative vertical displacements ranged from −0.082 m to −3.853 m, with the highest deformation recorded at benchmark R14. The calculated average annual subsidence rates reached values of up to −0.203 m/year and are reported as normalized indicators of cumulative deformation over the nineteen-year observation interval. The results demonstrate that mining-induced geomechanical instability may persist for decades after underground mining has ceased, emphasizing the necessity of long-term monitoring strategies in former coal mining regions affected by residual geological hazards. This study provides one of the few long-term GNSS field datasets documenting delayed mining-induced subsidence over a nineteen-year observation period in an underground coal basin, contributing rare field evidence of persistent post-mining geomechanical evolution in Eastern Europe. Full article

Unmanned aerial vehicles (UAVs) have become essential cyber-physical platforms for applications such as surveillance, infrastructure inspection, emergency response, and intelligent transportation. However, their tight coupling among sensing, communication, control, actuation, and swarm coordination also exposes them to sophisticated cyber-physical attacks that are difficult to detect using conventional intrusion detection systems. Existing machine learning, deep learning, graph-based, and federated intrusion detection approaches generally rely on statistical feature representations or temporal patterns, providing limited capability to model causal dependencies among interacting UAV subsystems and to generalize across heterogeneous operating environments. To address these limitations, this paper proposes Fed-CGIDS-UAV, a federated causal graph learning framework for cross-domain intrusion detection in cyber-physical UAV networks. The proposed framework models each telemetry window as a typed causal graph in which nodes represent navigation, sensing, communication, control, actuation, and swarm states, while directed edges capture stable operational dependencies. Intrusions are detected by identifying violations of these learned causal relationships, and the framework provides interpretable node-edge explanations to support root-cause analysis. Furthermore, federated learning enables collaborative model training across distributed UAV clients without sharing raw telemetry, thereby preserving data privacy while improving robustness under heterogeneous operating conditions. The proposed framework was implemented and experimentally evaluated in a controlled simulation environment covering four UAV operating domains and six representative attack classes. All experiments were repeated over five independent runs using different random seeds, and the reported results correspond to the measured average performance. The proposed framework was implemented using Python 3.12 (Python Software Foundation, Wilmington, DE, USA) and PyTorch 2.3 (Meta Platforms, Menlo Park, CA, USA). UAV flight data were generated using Microsoft AirSim 1.9.1 (Microsoft Corporation, Redmond, WA, USA), integrated with PX4 Autopilot v1.14 (Dronecode Foundation, San Francisco, CA, USA) and Gazebo Sim 11 (Open Source Robotics Foundation, Mountain View, CA, USA). Within this simulation-based evaluation, Fed-CGIDS-UAV achieved an accuracy of 0.968, an F1-score of 0.956, and an internal–external stability gap (IESG) of 0.028, outperforming conventional machine learning, deep learning, graph-based, and centralized causal baselines while maintaining competitive computational latency. Although these results demonstrate the effectiveness of the proposed framework under controlled simulation conditions, validation using real-flight UAV telemetry remains an important direction for future research. These results demonstrate that integrating causal graph learning with federated optimization provides an effective and interpretable solution for privacy-preserving intrusion detection in heterogeneous cyber-physical UAV environments. Full article

The BeiDou-3 Navigation Satellite System (BDS-3) broadcasts multi-frequency signals, including B1C, B2a, B1I, and B3I, offering a new technical approach for tilt monitoring of building structural safety. However, in building structural safety tilt monitoring based on the BDS-3 single-epoch algorithm, the engineering performance differences between the B1C/B2a new signal combination and the B1I/B3I traditional signal combination—in terms of monitoring accuracy, ambiguity fixing rate, computational efficiency, and tilt rate—have yet to be fully validated through comparative analysis. To address this issue, this paper proposes a building structural safety tilt monitoring method based on the BDS-3 single-epoch algorithm and conducts a field test on a multi-story building in Beijing. First, a BDS-3-based kinematic monitoring model is established, and an integer ambiguity error search band method based on the main and auxiliary frequencies is proposed. On this basis, three schemes are designed using medium Earth orbit (MEO), inclined geosynchronous orbit (IGSO), and geostationary Earth orbit (GEO) satellites, B1C/B2a (MEO/IGSO), B1I/B3I (MEO/IGSO), and B1I/B3I (MEO/IGSO/GEO), to comparatively analyze the accuracy, ambiguity fixing rate, computational efficiency, and measured tilt results of each scheme in building structural safety tilt monitoring. Experimental results show that all three schemes based on the BDS-3 single-epoch algorithm achieve millimeter-level monitoring accuracy and an ambiguity fixing rate exceeding 99%, with average computational times of 0.351 s, 0.338 s, and 4.572 s and corresponding building tilt rates of 0.22‰, 0.20‰, and 0.18‰, respectively, yielding an average tilt rate of 0.20‰. These results satisfy the 4‰ limit specified in the Code for Deformation Measurement of Building and Structure (JGJ 8-2016), thereby confirming the feasibility and effectiveness of the proposed method and offering a novel BDS-3 single-epoch algorithm for building tilt monitoring. Full article

Modern Global Navigation Satellite System (GNSS) architectures incorporate authorized signals like GPS M-code; however, conventional extraction methods suffer from performance degradation under low signal-to-noise-ratio (SNR) conditions and exhibit strong dependence on high-gain antennas and precise synchronization. This paper proposes a machine learning-based end-to-end extraction framework leveraging time-frequency domain feature fusion. This method breaks through the constraint of relying solely on either time-domain or frequency-domain features. It jointly feeds the time-domain waveforms and spectral features of baseband signals into models such as Multi-Layer Perceptron (MLP) and Transformer, enabling automatic learning of the nonlinear time-frequency characteristics of M-code. This approach effectively suppresses interference from P(Y) code sidelobes and fully exploits the information contained in both the main and side lobes of the M code spectrum. Experimental results demonstrate that under the extremely low SNR condition of −10 dB, the extraction accuracy of the proposed method is improved by 13.7% compared with conventional methods. Systematic accuracy–efficiency trade-off analysis shows that the lightweight MLP model achieves comparable accuracy to the complex Transformer model, with only 6.8% of the parameter scale and 6.2 times faster inference speed, making it the most competitive solution for real-time engineering deployment. In the real-world measurement scenario using a 7.5-m antenna, an extraction accuracy of 94.7% is achieved with only 10 ms of small-sample training data. This method significantly enhances the extraction performance of authorized signals under low-SNR non-cooperative reception conditions. Full article

Shipborne Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) integrated navigation based on Factor Graph Optimization (FGO) is increasingly used for maritime positioning, but its performance can be limited in low-dynamic sailing scenarios. In conventional GNSS/INS FGO without direct heading measurements, the heading state is mainly propagated by inertial measurements. During prolonged straight-line or low-maneuver sailing, position-related constraints provide only weak heading correction, which may lead to cumulative heading errors. In addition, existing learning-based GNSS outage compensation methods often introduce pseudo-GNSS observations with fixed covariance, making it difficult to represent the direction-dependent and time-varying uncertainty of data-driven predictions. To address these issues, this paper proposes a heteroscedastic TCN-Assisted GRU Network (HTAGN)-enhanced FGO framework for shipborne GNSS/INS/gyrocompass integrated navigation. Within the unified framework, a gyrocompass heading factor is constructed to provide persistent GNSS-independent heading constraints, and an HTAGN is designed to generate pseudo-GNSS position increments during GNSS outages. The direction-dependent standard deviations predicted by HTAGN are further mapped to the covariance matrix of pseudo-GNSS position factors, enabling confidence-adaptive factor weighting in the optimization process. The framework was validated on a single real-world sea trial with artificially simulated GNSS outages. The gyrocompass heading factor reduced the heading Root Mean Square Error (RMSE) by 84.6% compared with conventional GNSS/INS FGO. During 90 s and 180 s outages, the proposed method reduced the horizontal positioning RMSE by 63.7% and 52.5%, respectively, relative to the fixed-covariance TAGN baseline, demonstrating improved heading estimation and short-term positioning continuity under the tested conditions. Full article

Accurate measurement of the 6-DOF motion responses of marine floating structures is essential for structural safety assessment and operational decision-making. To address the critical issues of integration drift in inertial navigation systems, susceptibility of GNSS to sea-surface multipath effects, and deployment complexity of binocular vision systems, this paper proposed a 6-DOF motion measurement method for floating structures based on monocular vision and natural feature point tracking. This method eliminates the reliance on artificial cooperative targets and auxiliary sensors, instead utilizing the inherent surface textures of the floating structures as feature sources. Stable feature point tracking is achieved through multi-strategy cascaded detection and the pyramidal KLT optical flow algorithm. RANSAC geometric consistency verification is introduced to eliminate outlier matches, retaining only identical physical points between two consecutive frames for motion estimation. In-plane translations and $RZ$ angle are extracted from the similarity transformation, while $RX$ and $RY$ angles are estimated using principal component analysis of the covariance matrix of the feature point set. The depth-direction displacement is linearly mapped from variations in the scale factor. Subsequently, two series of physical model tests under different conditions were conducted to validate the measurement accuracy and robustness of the proposed method on different floating structures. The results demonstrate that the proposed method can accurately capture the motion attitudes of floating structures, maintaining a consistently high inlier ratio exceeding 80% in regular waves and averaging 85.2% in irregular waves, with a reprojection error of less than 0.05 pixels. The NRMSE for the primary motion directions are all below 10%, and the dominant frequency errors are essentially zero. It offers advantages such as low cost, easy deployment, and strong robustness, thereby providing valuable technical support for field monitoring of marine floating structures. Full article

Stratospheric airship navigation requires accurate wind field measurements at a ~20 km altitude, where low pressure and density limit the effectiveness of conventional wind sensors. To address this, we present a compact direct-detection Rayleigh Doppler wind lidar based on the molecular double-edge technique. The system utilizes a 532 nm fiber-coupled pulsed laser (0.5 W, 5 ns) and a fixed-cavity dual-channel Fabry–Perot etalon as the frequency discriminator. A liquid crystal variable retarder (LCVR) combined with a polarization beam splitter (PBS) enables non-mechanical, high-speed beam switching between two orthogonal line-of-sight (LOS) directions for horizontal wind measurement. Systematic tests are performed in controlled wind fields within Mie-dominated and Rayleigh-dominated regimes. The lidar effectively captures the sharp radial velocity profiles at wind speeds up to 7.6 m/s. Comparative experiments with a reference anemometer show that the system delivers reliable performance at 0.48 m range resolution, with measurement uncertainty below 0.34 m/s. With its compact, lightweight, and high-precision design, the developed lidar demonstrates reliable wind measurement capability under laboratory conditions, indicating its potential for future deployment on stratospheric airships. Full article

The gravity navigation reference map is a foundational element of gravity matching-aided navigation systems, as it directly determines positioning accuracy and overall navigation performance. To improve fused gravity anomalies and reduce reliance on manually selected parameters, this study proposes an adaptive multi-scale frequency-domain reconstruction (AMFR) algorithm. Specifically, the target ocean covered by multi-source satellite gravity data is divided into multiple scales, and distinct fusion parameters are assigned according to the characteristics of multi-source gravity data in different sea areas to enhance the accuracy of the fused gravity navigation reference map. Seven fusion schemes with different filtering diameters and weighting strategies are designed for comparative analysis. We evaluate the algorithm’s performance using measured gravity data with varying spatial distributions from the South China Sea and the Western Pacific Ocean, employing both RMS comparison and matching positioning experiments. Experimental results demonstrate that the RMS of the gravity navigation reference map constructed by the proposed AMFR algorithm is reduced by 30.4%, and the matching positioning accuracy is improved by 23.4%. The AMFR algorithm effectively enhances the precision of the gravity navigation reference map and improves the navigation performance of the gravity matching-aided navigation system. Full article

This research examines the fundamental autonomous inertial navigation formulas for aircraft. The study aims to identify analytical errors arising from the use of approximate gravity field models and proposes corrections for horizontal inertial components relative to changes in flight altitude. GPS measurements of ground speed and its total and relative derivatives are transformed into compensations for Coriolis and centrifugal accelerations, with flight altitude taken into account. This type of compensation corresponds to a precisely defined gravitational field model, assumed to be accurate to the second degree of eccentricity. Full article

Autonomous takeoff and landing (ATOL) remains one of the most challenging tasks for fixed-wing unmanned aerial vehicles (UAVs), particularly in environments where Global Navigation Satellite System (GNSS) signals are degraded or unavailable. This paper presents an LiDAR-assisted terrain-relative navigation framework for the autonomous takeoff and landing of a 5 kg fixed-wing UAV operating under degraded navigation conditions. The proposed architecture integrates a downward-facing LiDAR rangefinder with barometric altitude sensing, INS/GNSS navigation, optical-flow measurements, and airspeed information within a multi-sensor fusion and flight-control framework. The system combines a Pixhawk-based autopilot with a companion-computer architecture responsible for real-time sensor processing, altitude estimation, mission supervision, and MAVLink-based communication. A dedicated filtering strategy and sensor fusion approach enable reliable terrain-relative altitude estimation during critical low-altitude flight phases, while fault-tolerant command-management mechanisms improve operational robustness in the presence of temporary communication losses and sensor disturbances. The proposed framework was validated through Software-in-the-Loop (SITL), Hardware-in-the-Loop (HITL), and real-flight experiments. Experimental results demonstrated stable and repeatable autonomous landing performance. Comparative analyses showed that the LiDAR sensor provided the most accurate and responsive terrain-relative altitude measurements during takeoff, flare, and landing operations, particularly over irregular and vegetation-covered surfaces. In contrast, barometric sensing provided greater long-term stability during cruise flight, highlighting the importance of multi-sensor fusion for reliable altitude estimation throughout the mission profile. The results confirm that LiDAR-based terrain-relative sensing significantly improves autonomous takeoff and landing performance for fixed-wing UAVs operating in GNSS-degraded environments. The proposed architecture offers a practical and low-cost solution for the autonomous takeoff and landing of fixed-wing UAVs operating in GNSS-degraded environments while demonstrating the benefits of integrating LiDAR, inertial, barometric, and GNSS measurements within a unified multi-sensor autonomous flight framework. Full article