A Fast Jamming Strategy Optimization Method With Imperfect Experience

The primary objective of jamming strategy optimization is to ensure that a jammer timely finds an effective jamming strategy against the multifunction radar (MFR), thereby ensuring the safety of targets. Deep reinforcement learning (DRL) has been widely applied in solving the problem of jamming strategy optimization. However, the process still faces challenges such as low learning efficiency and a heavy memory burden. Therefore, we propose a fast jamming strategy optimization method with imperfect experience. Firstly, we model the radar countermeasure process as a Markov decision process (MDP), and formulate the jamming reward function by combining the jamming effectiveness and the jammer’s operational intent. Secondly, we design a novel hybrid jamming strategy choice module, which uses imperfect experience to improve the optimization efficiency of jamming strategy. Furthermore, to improve sample efficiency and reduce forgetting caused by small replay buffer, we respectively employ a mixed replay buffer strategy and a knowledge consolidation technique. Finally, extensive experiments demonstrate that under the guidance of imperfect experience, our proposed method achieves faster convergence speed and higher strategy accuracy compared with existing DRL-based methods.