DRL-Based Joint Path Planning and Jamming Power Allocation Optimization for Suppressing Netted Radar System
Shengxiang Li, Guangyi Liu, Kai Zhang, Zhisheng Qian, Siyuan Ding
Abstract
For jammer formations, due to advanced digital signal processing and precise synchronization, suppressing and penetrating netted radar systems face more challenges than ever. In this letter, a deep reinforcement learning-based joint path planning and jamming power allocation optimization (DRL-JPPAO) method is proposed. DRL-JPPAO models the penetration operation by a Markov Decision Process and utilizes the Proximal Policy Optimization algorithm to learn optimal sequential actions, which consist of waypoints and jamming power allocation matrices, to solve it. The learning process is guided by a reward function that depends on the success or failure of penetration and the distance to the target. Empirical results with a newly developed environment show that DRL-JPPAO can learn optimal paths and jamming power allocation strategies jointly, accomplishing the penetration operation effectively and elegantly.