Cooperative Tracking of Vessel Trajectory by Multi-Static Passive Stations Using an MC-RMPF

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.