Search Results (469)

Accurate radar tracking of highly maneuverable unmanned aerial vehicles (UAVs) is a key enabling technology for low-altitude airspace surveillance, counter-UAS defense, and UAS traffic management (UTM). Once a non-cooperative UAV has been detected, estimating its motion state must cope with nonlinear polar-coordinate observations, unknown maneuver-mode switching, and multi-scale state variations driven by agile drone flight, making it difficult for classical IMM/UKF filters and deep sequence models to preserve local maneuver response and long-term temporal consistency. We propose MSGate, a multi-scale gated temporal network organized along an “observation-representation-fusion-constraint” pipeline. A non-learnable physical front end maps polar measurements into a Cartesian observation trajectory of the same dimension as the UAV state. Multi-scale gated convolution and RoPE-Transformer encoding extract local maneuver responses and long-range dependencies. A shared gated dual-path decoder fuses the two paths adaptively at each time step and channel, and velocity-smoothness and position-velocity kinematic consistency terms regularize the predicted trajectory. On the real-UAV datasets UZH-FPV, EuRoC MAV, and NeuroBEM, under a unified range-azimuth observation protocol, MSGate attains the lowest average position and velocity errors (Pos-RMSE $0.0486\mathrm{m}$; Vel-RMSE $0.1767\mathrm{m}/\mathrm{s}$), outperforming the strongest time-series baseline TimeMixer, and generalizes to a separate nano-quadrotor dataset (NanoBench). MSGate provides an accurate, maneuver-robust solution for radar state estimation of highly maneuverable UAVs. Full article

Multi-camera vehicle sensing provides a cost-effective solution for 3D environmental perception in intelligent vehicles. A practical challenge is that temporal fusion becomes unreliable when the ego-vehicle undergoes aggressive motion. Historical camera features are commonly aligned by rigid ego-pose warping, but residual motion-induced displacement can still degrade object localization, heading estimation, and velocity sensing. This paper presents GCL-BEV, a motion-aware temporal compensation framework for multi-camera vehicle sensor systems. The proposed framework uses synchronized surround-view cameras and ego-motion measurements as coupled sensing inputs. First, a Geometric-Aware Feature Enhancement (GAFE) module converts ego-motion priors into motion-conditioned BEV sampling offsets, allowing the visual sensing representation to compensate for local temporal misalignment before fusion. Second, a View-Consistency Learning (VCL) objective imposes a training-time equivariance constraint so that the sensor representation remains consistent under planar viewpoint perturbations. Across 10 random seeds on nuScenes, GCL-BEV achieves 57.80% ± 0.15 NDS and 46.22% ± 0.16 mAP with a ResNet-101 backbone. Compared with BEVDet4D, it reduces the mean Average Orientation Error by 5.4% and shows smaller degradation from steady driving to high-turn scenarios, indicating improved robustness for dynamic vehicle sensing. Full article

Accurate velocity estimation in GPS-denied environments remains a core challenge for autonomous unmanned aerial vehicle (UAV) navigation. Our previous work demonstrated that fusing optical flow (OF) with dead reckoning (DR) substantially reduces position drift compared to inertial-only solutions. However, velocity estimates derived from dense optical flow are degraded by two systematic effects: (1) incorrect metric scaling when the camera footprint covers heterogeneous terrain types—particularly at forest–field transitions where the visible surface elevation differs significantly from bare-ground elevation; and (2) flow magnitude bias introduced by scene texture and structural properties. This paper presents three targeted improvements to a software pipeline for optical flow-assisted UAV navigation. First, single-point above-ground-level (AGL) estimation is replaced by camera footprint area mean sampling over co-registered Digital Terrain Model (DTM) and Digital Surface Model (DSM) rasters, with the surface model adopted consistently for OF metric scaling. Second, a one-dimensional Kalman filter with an innovation gate suppresses velocity spikes caused by abrupt terrain transitions. Third, a compact data-driven correction module uses selected flow, texture, and terrain descriptors to estimate a multiplicative velocity correction factor aligned with GPS-derived reference speed available during calibration and offline evaluation but not required during GPS-denied operation. Experiments on three real PX4-logged flight missions (Log 258 for calibration and Logs 259–260 for independent evaluation) totalling 7.3 min show terrain-dependent behaviour. On the mixed forest–field validation flight (Log 260), the improved pipeline reduces velocity mean absolute error (MAE) by 71% (1.04 m/s → 0.30 m/s) and dead-reckoning position MAE by 88% (59.8 m → 7.1 m), compared to the baseline from our previous work. On a flat open-terrain validation flight (Log 259), the terrain-aware modifications leave the terrain-insensitive baseline essentially unchanged, providing a control case for the proposed DSM-based scaling mechanism. Full article

To address the challenges of limited storage space and mobile launch-and-landing operations for vehicle-mounted multirotor UAV swarms, this paper proposes a stackable Cone-Sleeve UAV swarm system. The UAV airframe incorporates a through-body central sleeve integrated with conical guidance structures, which cooperate with a vertical guide rod mounted at the center of the mobile platform to achieve geometric passive pose correction during landing. In addition, a UWB-based onboard local positioning and navigation system is developed, in which a tightly coupled UWB/IMU estimator is employed to achieve high-precision relative state estimation for the UAV swarm. A five-stage finite state machine (FSM) schedules the landing sequence. During terminal landing, a motion feedforward control strategy is introduced for dynamic motion compensation and to ensure seamless state transitions. Simulation and vehicle-mounted experimental results demonstrate that, strictly under low-speed (≤0.5 m/s), constant-velocity straight-line motion conditions, the proposed system enables autonomous vertical takeoff and landing as well as rapid stacked launch-and-landing operations for multiple UAVs. The cooperative guidance strategy integrating active control and the Geometric Passive Guidance (GPG) mechanism improves the precision and speed of swarm landing operations. The proposed system provides a feasible system-level solution for the storage, transportation, and autonomous rapid launch-and-landing of high-density UAV swarms. Full article

Cooperative adaptive cruise control (CACC) platoons rely on Vehicle-to-Vehicle communication and GPS to maintain sub-second headways, creating cyberattack surfaces underrepresented in standard surrogate-safety metrics. We built a fully equation-based, Routh–Hurwitz- and Lp-string-stability-verified simulation of a ten-follower (eleven-vehicle, including the leader) CACC platoon (point-mass dynamics, actuator lag, PD spacing control) and subjected it to a two-channel GPS-spoofing attack corrupting both the attacked vehicle’s control loop and its broadcast position; velocity and acceleration broadcasts, and the CACC feed-forward term they drive, are left uncorrupted, so the reported boundaries are conditional on this restricted, single-channel threat model and should be read as a lower bound on attack severity rather than a worst case. Across a 64-cell severity–duration grid (2–20 m, 1–10 s; h = 0.6 s), minimum time-to-collision fell from 31.7 s to a simulated collision in 6/64 cells (9.4%), driven more by magnitude than duration; the disturbance decays sharply after the first hop rather than cascading down the platoon, so the resulting risk is local, not cascading. A 48-cell headway grid showed h ≥ 0.7 s eliminated all collisions at the originally tested attack duration (3/8 → 0/8 at fixed severity), a result that held under two alternative controller-gain sets tested for sensitivity and was largely, though not universally, robust to a substantially stiffer third set. A position sweep found risk invariant across nine of ten platoon positions. Batch-computed first-hop propagation and tail-to-origin amplification ratios showed the disturbance transiently amplifies (ratio > 1) at its first hop in a third of tested attacks despite decaying three orders of magnitude by the platoon’s tail, a behavior distinct from the front-injected Lp string stability verified separately. Peak root-mean-squared jerk stayed within the comfortable range (≤1 m/s³) in every tested cell, including collisions, showing collision and comfort risk are governed by different parameters. Embedding a representative detection and elastic-control layer alongside headway optimization eliminated collisions within the tested range and remained robust at three times that severity, where headway alone failed; because the detector’s residual is computed directly from the true offset magnitude and detector failure is not modeled, this joint-defense result is illustrative rather than a validated-detector-calibrated estimate. These results give a reproducible, quantified basis for headway- and detection-based mitigation policy in connected-vehicle platoons. Full article

This paper presents the development of a multi-domain simulation for the Porsche Taycan’s all-wheel-drive (AWD) electric propulsion system to investigate the impact of the rear drive unit’s two-speed transmission on performance and drive quality during maximum acceleration. This study was undertaken independent of the vehicle and propulsion system OEM. A lumped-parameter model of the front and rear electric drive units (EDU) and the high-voltage battery was developed and calibrated against the published data for key benchmarks, including 0–100 kph acceleration times and peak longitudinal acceleration. The mechanical shifting mechanism was reverse-engineered to simulate high-performance shift trajectories. To manage the transition, a clutch control scheme integrates a reduced-order clutch-to-clutch model featuring a feedforward (FF) torque estimator and a closed-loop feedback (FB) controller to achieve target input shaft speeds and shift durations. The study concludes with a comprehensive analysis of the propulsion system’s behavior at a battery state of charge of 96% and 25% and three electric motor speeds at which the upshift is commanded. The simulation results demonstrate that executing an early upshift at 10,700 rpm with 96% of SOC yields a 0.100-s inertia phase shift time, restricts the clutch thermal dissipation to 21 kJ, and achieves an 8-s velocity of 203.4 kph, outperforming the upshift at 15,300 rpm (0.210 s, 34 kJ, and 202.8 kph). Furthermore, the transient regenerative braking on the rear axle during the inertia phase reduces the peak current draw from 675 A to 87 A, recovering the DC bus voltage to enable cross-axle torque boosting on the front axle. Full article

This paper presents a universal, model-independent Adaptive PI (A-PI) control framework using multiple Recursive Least Squares (RLS) algorithms, primarily focused on the longitudinal velocity control of autonomous vehicles. To overcome the limitations of fixed-gain controllers, the proposed system self-tunes control parameters in real-time based on gradient descent and Lyapunov stability theories, requiring no complex vehicle dynamics. Particularly, to address the multivariable coupling effect during real-time estimation, a selective update rule is proposed, ensuring the theoretical validity of independent gain self-tuning. Furthermore, a novel error-based covariance scaling logic is introduced to dynamically and selectively update the proportional and integral scale factors across three distinct error areas. This mechanism ensures rapid initial convergence in the transient region and smooth settling without overshoot near the target. To evaluate the feasibility of the proposed universal and adaptive framework, longitudinal velocity tracking performance was analyzed through a MATLAB/Simulink version 2023b and CarMaker co-simulation environment. Simulation results demonstrate that the proposed A-PI controller significantly reduces the Root Mean Square (RMS) control error compared with conventional fixed PI controllers under dynamic scenarios, proving its robust adaptability and paving the way for future integrated longitudinal and lateral vehicle control. Full article

This paper presents a positioning control method for underactuated unmanned surface vehicles (USVs) subject to environmental disturbances and wave-contaminated measurements. In underactuated dynamic positioning, surge motion and yaw motion can be directly regulated by the propulsion system, whereas sway motion cannot be directly controlled by an independent lateral thrust. Therefore, the lateral environmental-force component is utilized to induce the vehicle’s dynamic response to sway. To achieve this objective, a position-keeping guidance system taking into account the desired heading and the lateral positioning error is introduced in this paper. With this guidance mechanism, the lateral environmental-force component can drive the USV to reduce the cross-track error, thereby enabling underactuated positioning. A rotated coordinate system is established around the desired position, and the positioning error is decomposed into along-track and cross-track components. To improve the transient response, an error-rate feedback term is introduced into the rotated-angle update law for yaw-heading guidance design, which enhances the damping of the cross-track dynamics. Meanwhile, a wave-filtering observer is designed to make low-frequency position and velocity estimates for feedback control. Simulation results under multiple operating conditions show that the proposed observer reduces the amplitude and high-frequency variation of the control signals compared with the existing wave-filtering observer, and the proposed positioning control method achieves smaller positioning errors than the existing nonlinear positioning control (NPC). The comparative results also indicate that the proposed method is suitable for position keeping under constant or slowly varying environmental loads, moderate model uncertainty, and wave-contaminated measurements, whereas rapidly varying load directions may degrade the positioning accuracy. Full article

Reliable fault detection and isolation (FDI) for actuators and sensors in underactuated autonomous underwater vehicles (AUVs) is challenging because nonlinear hydrodynamic coupling redistributes fault signatures, and persistent ocean currents can trigger false alarms. This paper presents a reinforcement learning (RL)-augmented dual-layer FDI framework for actuator and sensor faults in underactuated AUVs. The actuator layer uses a robust adaptive propagated interval observer (RAPIO) that evaluates thruster and control-surface residuals against a calibrated dynamics-consistency tube. The sensor layer forms estimator-consistency residuals for Doppler velocity log (DVL), depth, and inertial measurement unit (IMU) measurements against a reference-separated finite-time extended state observer (FTESO). An offline-trained soft actor–critic (SAC) policy schedules bounded actuator uncertainty margins and sensor alarm thresholds according to operating confidence. The scheduled actuator error dynamics remain Metzler and Hurwitz, preserving positive interval propagation and center-error input-to-state stability (ISS) independent of policy convergence. A Schmitt-trigger alarm and signal-space disambiguation rule classify healthy, actuator-only, sensor-only, and simultaneous-fault conditions under explicit residual-separation and persistence conditions. Across 72 simultaneous-fault episodes over a $4\times 6$ uncertainty–current grid, the proposed method achieved $100\%$ detection coverage for both actuator and sensor faults with only five false-alarm events, retaining full coverage in the severe-current, high-uncertainty subset where the selected actuator and sensor baselines achieved only 88.9% and 70.4% detection, respectively, with more false alarms. These results indicate that the proposed bounded RL scheduler can deliver reliable, certifiable actuator and sensor fault diagnosis under significant operational uncertainty. Full article

This paper investigates the trajectory tracking control problem for an unmanned surface vehicle (USV) subject to unknown time-varying environmental disturbances and the challenge of ensuring fast convergence while avoiding singularities. A novel fixed-time nonsingular fast terminal sliding mode (NFTSM) control scheme, integrated with a fixed-time disturbance observer (DOB), is proposed to achieve high-precision and robust tracking. The control architecture consists of three key components: (i) a fixed-time virtual velocity control law designed to ensure that position errors converge to zero within a fixed time even when velocity errors vanish, thereby preventing slow convergence; (ii) a nonsingular fast terminal sliding surface that eliminates the singularity issue inherent in traditional terminal sliding mode and guarantees that the USV state converges to the desired trajectory within a fixed time independent of initial conditions; and (iii) a fixed-time DOB that accurately estimates and compensates for external disturbances, with estimation errors proven to converge to zero in a fixed time. The stability and fixed-time convergence of the closed-loop system are rigorously established using Lyapunov theory. Comparative simulations, conducted under external disturbances on the Cybership II model, demonstrate that the proposed NFTSMC strategy significantly outperforms conventional sliding mode control (SMC) and nonsingular terminal sliding mode control (NTSMC). Specifically, the proposed scheme reduces the integral of absolute error (IAE) for position tracking by over 60% and the integral of time-weighted absolute error (ITAE) by more than 30% compared to SMC, while achieving smoother control inputs and stronger disturbance rejection. These results highlight the superior convergence speed, tracking accuracy, and robustness of the proposed controller, underscoring its originality and practical value for USV autonomous navigation. Full article

This article concentrates on the practical prescribed-time leader-following consensus problem of second-order multi-agent systems (MASs) under disconnected time-varying topologies. Firstly, a new practical prescribed-time stability criterion is presented, where the time-varying scaling function is related to connected moments and the derivative inequality is increasing in the disconnected intervals. Secondly, inspired by the backstepping design framework, the practical prescribed-time control protocol is designed, including the distributed velocity estimator, virtual velocity and dynamic event-triggered controller. Thirdly, some consensus conditions for achieving leader-following consensus are established and the Zeno behavior is excluded. Finally, simulation results on unmanned aerial vehicle (UAV) formation tracking are provided to demonstrate the effectiveness of the proposed method. Full article

Unauthorized unmanned aerial vehicle (UAV) intrusions in sensitive areas such as airports have made accurate UAV detection and localization a pressing need. Acoustic sensing is passive and weather-independent, but conventional microphone arrays require many elements and a large aperture. This paper proposes an acoustic vector sensor (AVS)-based method, termed Covariance Enhancement and Confidence-guided Tracking for 3D Acoustic Localization (CECT-3DAL). A single AVS measures the sound pressure and three-axis particle velocity at one point. Adaptive diagonal loading improves the robustness of the covariance matrix at a low signal-to-noise ratio (SNR). An exponential spectral enhancement strategy sharpens the spatial spectrum peaks for direction estimation, and an eigenvalue-ratio-based confidence drives confidence-weighted smoothing of the angle sequences. Meanwhile, a dual-sensor geometric model provides a closed-form three-dimensional solution. In simulations, the azimuth and elevation root-mean-square errors (RMSEs) were below 1.5° for SNR above 4 dB. In an anechoic chamber, confidence-weighted smoothing reduced the azimuth and elevation standard deviations from 4.34° and 2.63° to 1.46° and 0.86°. In field experiments, the hovering azimuth stayed within a 90% span of 2–3.5°, with an average horizontal RMSE of 0.209 m against a GPS reference, and trajectories under various flight modes remained continuous and smooth. The proposed method offers a compact, passive, and low-cost solution for counter-UAV acoustic surveillance. Full article

High-speed Unmanned Aerial Vehicles (UAVs), characterized by high velocity and maneuverability, represent critical strategic threats within the aerospace security domain. Accurate trajectory prediction is a fundamental prerequisite for effective early warning and decision-making in defense. Despite increasing research in the domain, model-, data-, and hybrid-driven methods have not yet been comprehensively examined under a unified framework, limiting the understanding of their relative strengths and applicability. To address this gap, this paper systematically reviews the evolution and current technical status of these paradigms, categorized by the core logic underlying prediction methods. The principles and applicability limits of physical process modeling and state estimation algorithms are analyzed, along with emerging applications of machine learning and deep learning for trajectory feature extraction and pattern recognition. State-of-the-art architectures involving the integration of physical constraints and data-driven learning are discussed. Standard evaluation metrics are introduced to facilitate performance benchmarking of existing methods. Comparative analysis reveals that no single technical route can fully address the coupled challenges of uncertainty, accuracy, and real-time performance, underscoring that hybrid frameworks are essential for balancing these competing requirements. Lastly, key challenges are summarized, and future research directions are outlined to advance trajectory prediction methodologies. The provided insights can inform method selection and promote the development of high-accuracy prediction systems. Full article

This work develops a tool to efficiently estimate the diffractive loads on a quadrotor uncrewed aerial vehicle (UAV) immediately following a nearby explosion. Existing models in the literature that predict the time history of wind velocity and the overpressure at a single distance from the blast are extended to model a moving blast wave that passes over the vehicle. The time-varying diffractive loads (i.e., due to the blast-induced pressure differential) are first modeled for a single sphere in a blast wave and then for a quadrotor approximated as a series of spheres connected by rods—one sphere for each of the four motors and one sphere for the central body. The overpressure and wind velocity models are compared with computational fluid dynamics (CFD) data. To illustrate the computational approach, a representative quadrotor model is perturbed by a blast from an initial hover flight condition in simulation. The rigid body dynamics are simulated over a short duration (ninety milliseconds) to determine the UAV’s state immediately after the explosion has concluded. The vehicle state history is predicted under the assumption of diffractive loads with a quadratic drag model and constant hover thrust. Full article

Road traffic accidents continue to be a major global cause of fatalities, disproportionately affecting pedestrians and other vulnerable road users. While deep reinforcement learning has proven effective in handling complex navigation tasks, providing formal stability and safety guarantees during both training and deployment remains a significant challenge. This paper introduces a dual-layer safety-aware framework for pedestrian avoidance in occluded urban driving. During training, a first-order Control Lyapunov–Barrier Function is integrated with Proximal Policy Optimization to promote goal-reaching stability and obstacle avoidance: the analytic Lie derivatives of the Lyapunov and barrier functions are embedded as a modifier in the advantage estimate, providing explicit stability and safety signals that accelerate convergence toward safe, goal-reaching behavior without disrupting the standard policy update. At deployment, a higher-order Control Lyapunov–Barrier Function, realized through a quadratic programming safety filter, acts as a safety shield that projects the nominal acceleration onto the intersection of the second-order Lyapunov and barrier feasibility sets; the barrier function is further extended with relative velocity terms to account for dynamic pedestrian motion. Experiments with a four-wheeled vehicle in the Webots simulator show that the framework reliably reaches the goal, avoids an occluded pedestrian across a range of crossing speeds, and improves task success rates and safety-constraint adherence relative to Proximal Policy Optimization and a conventional higher-order safety filter baseline, particularly during emergency braking maneuvers. Full article