Airborne Radar Trajectory Optimization Based on Deep Reinforcement Learning in Extended Target Tracking

In this article, an effective algorithm is proposed for trajectory optimization in airborne radar for extended target tracking (ETT). The key idea of the proposed airborne radar trajectory planning (ARTP) scheme is to model this problem as a partially observable Markov decision process (POMDP) and plan the waypoints of airborne radar using deep reinforcement learning (DRL) in order to improve the target’s kinematic state and shape tracking accuracy, subject to the airborne radar maneuverability limitation. Specifically, at each tracking interval, the predicted Gaussian Wasserstein Distance (GWD) is analytically calculated and subsequently adopted as a reward function to evaluate the performance of extended target tracking. Then the DRL agent autonomously learns trajectory planning strategies to maximize ETT tracking performance. Simulation results demonstrate that the proposed ARTP algorithm can deliver superior performance in terms of maximizing the overall ETT performance compared with other benchmarks.