A Reinforcement Learning Approach to Decision Making of Phased Array Radar Jamming

With the rapid advancement of phased array radar, the guidance and anti-jamming capabilities of phased array radar seekers have been further enhanced. Traditional radar jamming decision-making methods are no longer applicable in electronic warfare. Thus, radar jamming decision-making methods based on reinforcement learning have emerged, which can effectively address the issues of poor performance and low efficiency of traditional jamming approaches. Given the extremely high cost and poor repeatability of physical experiments, the construction of simulation models is frequently adopted for simulation experiments. Unlike most other studies on radar jamming decision-making that employ functional-level simulation modeling, this paper adopts signal-level simulation of radar jamming decision-making, presenting a more realistic and intuitive reflection of the entire process of interference equipment interfering with the missile terminal guidance. Through the signal-level simulation of phased array radar terminal guidance and the introduction of reinforcement learning algorithms, this study investigates how the interference equipment should act to maximize the interference benefit. Through simulation experiments, on the one hand, it is demonstrated that the reinforcement learning algorithm can improve the interference effect. On the other hand, the accuracy of the signal-level simulation model is verified.