Radar Jamming Decision-Making in Cognitive Electronic Warfare: A Review
Chudi Zhang, Lei Wang, Rundong Jiang, Jun Hu, Shiyou Xu
Abstract
With the increasingly complex electromagnetic environment and the intelligent development of radar, the jammer, as opposed to radar, urgently needs to improve its ability to recognize threat targets and make jamming decisions. In this article, we first introduce the concepts and systems of cognitive electronic warfare (CEW) and summarize its research status. Through analysis of the existing CEW systems, we propose a CEW model suitable for cluster confrontation scenarios. Then, for the radar jamming decision-making (RJDM), namely, a crucial part of CEW, we discuss the advantages, disadvantages, and applications of the traditional methods and analyze the machine-learning-based methods, including Markov decision processing, the newest Q-learning, deep Q-learning (DQN), double DQN (DDQN), asynchronous advantage actor-critic (A3C) algorithms, and their improved algorithms. We build radar adversarial models and verify the effectiveness of reinforcement learning (RL) algorithm and the superiority of deep RL by simulating both the underlying Q-learning and DQN algorithms. Finally, the research trends of CEW are discussed.