Radar‐Physics‐Guided Deep Reinforcement Learning for Adaptive Cooperative Multi‐Radar Fusion and Maneuvering Target Tracking Under Time‐Varying Sensing Conditions

ABSTRACT

Multi radar cooperative tracking leverages spatial diversity and measurement redundancy to enhance detection reliability and state estimation accuracy. Nevertheless, most existing fusion schemes adopt fixed or heuristically tuned weights and rely on simplified statistical assumptions, which limits robustness in non‐stationary electromagnetic environments where station wise SNR varies rapidly. This paper presents a deep reinforcement learning (DRL) driven adaptive fusion framework that jointly optimises measurement fusion weights and tracking‐filter parameters. A central feature is a radar‐physics‐guided inversion‐matching feedback loop that explicitly couples the fused Cartesian state with each radar's local measurement domain. Specifically, the fused estimate is back projected to local polar coordinates to compute spatio‐temporal residuals and Mahalanobis distance consistency statistics. Together with real‐time SNR and normalised innovation squared (NIS), these physically interpretable signals form the state representation for a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent. We further design a multi objective reward to trade off position accuracy, velocity fidelity, tracking smoothness and uncertainty reduction. Simulations under heterogeneous sensing conditions verify that the proposed method improves adaptive weighting behaviour and preserves trajectory continuity compared with conventional baselines.