Search Results (202)

This paper considers a numerically stable discrete-time representation of the three-dimensional Constant Turn (CT) motion model within the Interacting Multiple Model (IMM) framework for radar tracking of maneuvering aerial targets. Classical discrete CT models used in Kalman-filter-based tracking contain singular expressions in the vicinity of zero and near-zero turn rates, which may degrade estimation accuracy and impair numerical robustness. To address this problem, a Maclaurin-series-based discretization of the three-dimensional CT model is developed, in which the state transition matrix and the process-noise-related matrices are approximated in polynomial form. Linear, quadratic, and cubic approximations are constructed and analyzed. The proposed CT model is integrated into a three-model IMM algorithm together with the Constant Velocity (CV) and Constant Acceleration (CA) models. The study includes both an internal comparison of Maclaurin approximations of different orders and an external comparison with the classical CT discretization and a Padé-based reference discretization. Numerical experiments are performed for representative three-dimensional maneuvering scenarios under radar measurement conditions. The obtained results show that the proposed discretization eliminates singular behavior near zero turn rate while preserving the tracking capability of the IMM estimator. The comparative analysis demonstrates that the quadratic Maclaurin approximation provides the most favorable trade-off between modeling accuracy, numerical stability, and computational cost. It yields tracking performance close to higher-order approximations and competitive with the Padé-based reference approach, while remaining simpler for practical implementation in real-time radar tracking systems. These results indicate that the proposed quadratic approximation is a suitable solution for maneuvering aerial target tracking in three-dimensional radar applications. Full article

Traditional maritime vessel tracking methods based on multi-static passive radar stations typically process all available observations, leading to substantial computational overhead and estimation variance. Furthermore, discrepancies in refresh rates and noise levels among stations often cause significant jumps in estimated positions between updates, resulting in trajectory discontinuities. To mitigate these issues, this paper introduces a multi-station cooperative vessel tracking framework based on a motion-constrained resample–move particle filter (MC-RMPF). In the proposed method, systematic resampling is first used to alleviate particle degeneracy, and a markov chain monte carlo (MCMC) move step is subsequently applied to rejuvenate the resampled particles under vessel-motion feasibility constraints. Additionally, a distributed detection network is constructed using directional data from multiple stations, dynamically selecting optimal observation subsets to balance localization accuracy with computational load. The experimental results demonstrate that, compared to the baseline methods, our method reduces the Root Mean Square Error and Circular Error Probability of position tracking by 23.5% and 21.7%, respectively. It exhibits strong reliability in challenging scenarios such as target maneuvers and temporary observation loss. Full article

To address tracking accuracy degradation caused by noise in sensor observations, a maneuvering target tracking algorithm based on an improved Received Signal Strength Indicator (RSSI) ranging model is proposed for Wireless Sensor Networks (WSNs). The traditional deterministic ranging model is replaced by a backpropagation neural network optimized via the Osprey Optimization Algorithm (OOA-BP), which directly maps noisy RSSI measurements to precise physical distances. Filtering and tracking are executed using an Extended Kalman Filter (EKF) combined with a uniform circular motion model, demonstrating the robustness of the observation model across dynamic predictions. Simulation results validate the efficacy of the proposed framework. In the distance estimation phase, the OOA-BP model reduces the average ranging error to 0.04 m. During dynamic tracking, the integrated OOA-BP-EKF architecture demonstrates superior tracking performance compared to standard frameworks, reducing the Root Mean Square Error (RMSE) by 15.33% and 59.89% compared to GA-BP and standard BP algorithms, respectively. Full article

Prediction of the reachable domain for high-threat unmanned aerial vehicles (UAVs) is critical for enabling cross-domain flight vehicles to perform proactive avoidance maneuvers. To address this challenge, this paper proposes a novel generic framework that integrates a Radau pseudospectral method (RPM) with a BP neural network, supported by information acquired from satellites. The framework begins by estimating a preliminary state vector of the non-cooperative target, including its coarse position and velocity, via a Newton iterative algorithm. To refine this initial estimate and enable continuous tracking, an Extended Kalman Filter (EKF) is fused with a flight vehicle dynamics model. Subsequently, the RPM is employed to solve the trajectory planning problem, generating a comprehensive database for offline training. This database is then used to train a multilayer feedforward neural network within an offline training and online application framework, which drastically reduces computational complexity and time. Finally, numerical simulations demonstrate the method’s high prediction accuracy and strong robustness against tracking uncertainties. Crucially, the neural network predicts the reachable domain in just 0.01 s, making it highly viable for real-time online applications. Full article

Under strict spatial constraints and environmental interference, autonomous navigation of vessels in inland bridge-restricted waterways demands precise coordination between collision avoidance and trajectory tracking. To meet these operational demands, an integrated framework that directly combines spatiotemporal risk assessment with dynamic control execution is developed. Based on a digital traffic model integrating bridge piers and channel boundaries, collision risks are evaluated by combining trajectory-predicted time to safe distance with the velocity obstacle interval. Such a formulation quantifies the actual spatial difficulty of evasion rather than relying solely on temporal urgency. Driven by this continuous assessment, a time-series rolling strategy calculates feasible maneuvering intervals, generating trajectories that comply strictly with inland navigation rules and physical vessel limits. Subsequently, an adaptive model predictive control algorithm executes these commands, implicitly compensating for the localized hydrodynamic disturbances typical of bridge areas. The effectiveness of the architecture is validated through comprehensive simulations covering rule-based encounters and complex multi-vessel scenarios. Quantitative results indicate that under wind and current disturbances, the maximum route tracking deviation is constrained below 53 m, while the minimum encounter distance with target ships is consistently maintained above 51 m. These performance metrics confirm the capacity to execute safe, rule-compliant maneuvers while preserving high navigational precision in confined inland environments. Full article

Owing to multi-target tracking in scenarios with nonlinearity, uncertain measurement model and high clutter density, the Poisson multi-Bernoulli mixture (PMBM) recursion is prone to unstable covariance propagation under nonlinear dynamics as well as uncertainty in measurement-to-target association caused by mismatched gate that causes erroneous updates from clutters. In the prediction stage, the singular value decomposition (SVD) is used in place of Cholesky factorization to construct and propagate the square-root covariance factor in the square-root cubature Kalman filter (SCKF), yielding a numerically stable square-root implementation. Then, the resulting SVD-SCKF is incorporated into the PMBM prediction step and used to propagate the Gaussian-mixture components of both the Poisson point process (PPP) intensity and the Bernoulli component in the Multi-Bernoulli mixture (MBM), yielding predicted means and covariances under nonlinear dynamics. An adaptive fading factor is determined from innovation statistics, and covariance inflation is performed to improve robustness under target maneuvers and model mismatch. In the update stage, the unknown measurement function is regressed by Gaussian process (GP) using historical state–measurement samples, yielding an equivalent measurement mapping and state-dependent uncertainty. Furthermore, the predicted measurement distribution is generated from the GP-based conditional measurement distribution with state prior approximated by SVD-SCKF cubature points. An adaptive gate is determined from the GP-based conditional measurement distribution, which is approximated by an equivalent ellipsoidal gate via fitting for screening the current measurements and filtering out clutter. Residual in-gate clutter measurements are handled via Bayesian target discrimination, where the posterior probability of measurement originated from target is employed as a weight and incorporated into association weights and update likelihoods. Simulation results further confirm the effectiveness and stability of the proposed filter in complex scenarios. Full article

In the context of an increasingly chaotic electromagnetic environment, the problem of multisensor data fusion for tracking airborne maneuvering targets has garnered significant attention and applications. In low-to-no power scenarios, certain sensors exhibit measurement inaccuracies, and the disparity in measurement precision among networked sensors leads to data inequality. This results in poor fusion accuracy in the multisensor fusion process, particularly when prior weights are unknown. To address the aforementioned problems, this study first redefines the motion model of airborne maneuvering targets by capturing the complexity of the trajectory of the target. Subsequently, a modeling framework for low-to-no power scenarios is established using a one-transmitter three-receiver radar system. In this model, the Signal-to-Noise Ratio (SNR) of the two sensors was intentionally reduced to simulate data inequality. Finally, a distance velocity (DV) fusion algorithm was designed based on the Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) algorithm. Specifically, after the state extraction step of the SMC-PHD filter algorithm, the final estimated target was obtained in two steps: judgment and weighted summation. The simulation results demonstrate the effectiveness of the proposed algorithm in improving fusion accuracy and robustness in dynamic environments and under real electromagnetic interference. Full article

Urban roundabouts present significant design challenges for the integration of micro-mobility, yet comparative evidence regarding user behavior remains limited. As cities transition toward sustainable transport networks, understanding the operational needs of different micromobility modes is essential for urban planning. This study investigates the dynamic strategies of micromobility users through a controlled field experiment at a mini-roundabout in Gravina di Catania, Italy. Twenty experienced riders executed crossings using conventional bicycles and electric scooters. Utilizing drone recordings and open-source tracking, the analysis extracted speed, longitudinal acceleration, and path radius across 80 maneuvers. The findings reveal that behavior is highly dependent on vehicle type and geometric deflection. On highly deflected trajectories, e-scooters selected wider radii and achieved up to 15% higher speeds and accelerations than bicycles, whereas on gentler trajectories, they adopted more conservative, tighter lines with intense braking. Bicycles exhibited smaller, less systematic adjustments. These significant kinematic differences indicate that bicycles and e-scooters possess distinct performance envelopes. Treating them as a single legal or design class obscures stability disparities influencing conflict risk. Ultimately, this research provides empirical insights to guide urban planners in redesigning intersections, emphasizing that tailored infrastructure and targeted speed management are critical steps toward safer, truly sustainable urban mobility. Full article

This paper presents a closed-form tracking and collision avoidance framework for multi-agent differential drive robots. Existing reactive methods often rely on purely geometric proximity, leading to conservative detours and local minima. A state-dependent adaptive avoidance strategy is developed to dynamically modulate repulsive forces using the time-derivative of fractional barrier risk functions, alleviating unnecessary evasive maneuvers. Within a convergence vector field (CVF) architecture, an active safety-corrected tracking mechanism orthogonally strips hazardous velocity projections from the spatial error. This mitigates the inherent conflict between target tracking and obstacle repulsion. A matrix projection-based Lyapunov approach demonstrates the finite-time convergence of the vehicle orientation, bounded tracking errors, and collision-free properties of the closed-loop system, with effectiveness further validated through simulations. Full article

The increasing deployment of underwater vehicles demands accurate and energy-efficient target tracking in sensor networks. However, existing approaches have largely addressed tracking accuracy and energy efficiency in isolation, and a system-level framework that jointly optimizes both remains lacking. To address this gap, this paper proposes a joint optimization framework with two main contributions. First, to improve tracking accuracy under complex maneuvering conditions, we develop an Interactive Multi-Model using Long Short-Term Memory Classification (IMM-LSTM-C) algorithm, which integrates multi-step model likelihoods into an LSTM network for precise motion classification, achieving a 7.1% accuracy improvement over IMM-BP. Second, to reduce network energy consumption while maintaining tracking performance, we introduce an Improved Binary Prairie Dog Optimization (IBPDO) algorithm for node selection, enhanced with Cauchy mutation and opposition-based learning. Simulation results show that IBPDO achieves 6.1–8.2% higher accuracy than BWOA and reduces energy consumption by 12% compared to LNS. Furthermore, the complete joint framework demonstrates synergistic effects, reducing tracking error by 19.3% and energy consumption by 15.4% over the IMM + LNS baseline. The proposed framework provides an effective balance between tracking accuracy and energy efficiency in underwater acoustic sensor networks. Full article

Aiming to improve radar multi-target tracking (MTT) accuracy and association performance in complex scenarios involving dense clutter, missed detections, and maneuvering targets, an improved tracklet generation approach based on the expectation–maximization (EM) framework is proposed in which data association variables and motion model variables are jointly modeled as latent variables. These variables are estimated through iterative updates based on the loopy belief propagation (LBP) algorithm and the interacting multiple model (IMM) filtering and smoothing algorithms to generate high-confidence tracklets. Then, a delayed decision-making strategy based on the multi-hypothesis approach is employed to associate these tracklets into complete target trajectories. The resulting algorithm is named IMM-TrackletMHT. The performance of the IMM-TrackletMHT algorithm is evaluated and compared with several baseline algorithms in simulated scenarios under different clutter rates and detection probabilities. The simulation results demonstrate that the proposed algorithm consistently outperforms the baseline methods in terms of tracking accuracy, exhibits strong robustness to variations in the operating environment, and achieves higher computational efficiency in multi-scan measurement processing, thereby demonstrating the effectiveness and superiority of the proposed tracklet generation approach for maneuvering MTT. Full article

This paper presents the design and implementation of a real-time visual tracking system for unmanned aerial vehicles (UAVs), based on the DJIPayload Software Development Kit (PSDK), addressing the challenge of balancing high precision with low latency on resource-constrained edge platforms. By utilizing DJI PSDK to abandon the Robot Operating System (ROS) layer and its associated serialization overhead, the proposed Middleware-Free Architecture reduces end-to-end latency by over 60% to approximately 30 ms. To address computational constraints, a Lightweight Asymmetric De-coupled Visual Servoing (ADVS) strategy is proposed. It adopts orthogonal kinematic de-coupling to bypass Jacobian matrix inversion and integrates a non-linear dead-zone mechanism with dynamics-aware gain scheduling to compensate for sensing anisotropy and gravitational nonlinearity. Simultaneously, a Geometry-Aware Fusion strategy is employed to reject visual outliers, while a Finite State Machine (FSM) strictly enforces temporal consistency. Field experiments in various scenarios verify the system’s stability and tracking capability. Specifically, the platform maintains a robust lock on targets at speeds up to 23 m/s across dynamic maneuvers. The successful implementation of this system confirms that high-performance edge tracking does not rely solely on the scaling of visual model complexity but can also be effectively achieved through the architectural minimization of latency combined with the optimization of theoretically grounded robust control strategies. Full article

The FOCUS mission is an integrative project developed at the Universidad Nacional de San Martín (UNSAM), Argentina, featuring a constellation of small satellites equipped with X-band Synthetic Aperture Radar (SAR) sensors. Designed with autonomous orbit control, the mission enables Interferometric SAR (InSAR) applications for critical infrastructure monitoring, providing scalable and cost-effective global observation capabilities. This paper presents the modeling, design, and numerical evaluation of the Attitude and Orbit Determination and Control System (AODCS) for the FOCUS mission. The analysis incorporates realistic constraints, including actuator saturation, sensor noise, underactuation effects, and hardware limitations—specifically regarding magnetorquer magnetic moments, reaction wheel capacities, and propulsion unit impulse bounds. Utilizing the NASA 42 attitude and orbit simulator, numerical simulations were conducted to assess stability, pointing accuracy, and agile maneuver tracking through specialized guidance laws. The results confirm that the proposed AODCS architecture achieves stable, responsive performance and supports continuous orbit maintenance, ensuring adequate target acquisition per orbit. Additionally, the selection of star trackers allows achieving a secondary objective through the detection of Resident Space Objects. Full article

In order to evade interception, advanced aircraft often adopt jump-gliding trajectories to efficiently utilize aerodynamics and achieve complex maneuvers. Precise guidance of UAVs for intercepting such targets is critically challenged due to their high speed and uncertain maneuvers. For terminal guidance scenarios, the extremely short engagement window necessitates strict convergence within the predefined finite time. While PPC offers a promising framework to ensure such convergence with guaranteed transient performance, it suffers from singularity when target uncertainties drive tracking errors beyond performance bounds. To address these challenges, this paper proposes an adaptive prescribed-performance guidance law with predefined-time convergence for UAVs. Built upon the analysis that jump-gliding targets exhibit predominantly longitudinal oscillatory maneuvers, we first establish a velocity model to characterize their motion uncertainties. Using the derived uncertainty bounds and estimated parameters, a predefined-time performance function (PPF) is then developed and robustly modified to eliminate the singularity risk. By integrating this modified PPC with an adaptive law, the proposed framework achieves robust predefined-time convergence of the line-of-sight angle while simultaneously compensating for unknown target maneuvers. Theoretical analysis verifies the framework’s stability, and simulation results demonstrate its effectiveness in intercepting highly maneuverable targets. Full article

Unmanned aerial vehicle (UAV) guidance systems must operate reliably under significant uncertainties, such as sensor noise, target maneuvers, and environmental disturbances. Traditional guidance methods like proportional navigation (PN), while computationally efficient, often struggle to maintain performance under such challenging conditions. To overcome these limitations, this study proposes a robust UAV guidance framework based on deep reinforcement learning (DRL), specifically utilizing the soft actor–critic (SAC) algorithm. The UAV–target tracking problem is formulated as the Markov decision process (MDP) for both two-dimensional (2D) and three-dimensional (3D) scenarios. A deep neural network policy is trained in noisy environments to generate acceleration commands that minimize the zero-effort miss (ZEM). Extensive numerical simulations conducted using the OpenAI Gym validate effectiveness of the proposed method under previously unseen initial conditions and increased noise levels. The results demonstrate that the SAC-based policy achieves higher tracking success rates than the PN, particularly under strict terminal conditions and observation noise. Full article