Deep reinforcement learning based decision making for radar jamming suppression

Abstract

Due to the need for the real-time participation of a large number of professionals, existing decision-making methods for radar interference suppression are characterized by a slow decision-making speed, unstable decision-making effects, and insufficient decision-making intelligence. In this paper, a deep reinforcement learning (DRL)-based decision-making method for radar interference suppression is proposed. This method has a fast decision speed and a stable and accurate decision effect, and can complete decision-making tasks by itself with high intelligence. To enhance the ability of the agent to acquire high-value experiences, the variable greedy algorithm (VGA) is proposed. The VGA adjusts the fixed greedy value in the original action strategy into a declining greedy curve that mimics the human learning process via a combination of the ideas in the win or learn fast–policy hill-climbing (WoLF-PHC) algorithm and the Ebbinghaus forgetting curve. To improve the efficiency of the agent in utilizing high-value experiences, the double-depth prioritized experience replay (DDPER) algorithm is proposed. The DDPER algorithm changes the uniform random experience replay into prioritized experience replay (PER), and performs sorting and extraction learning based on experience values in the form of additive trees to achieve better learning results. Further, the accuracy and speed of decision-making are improved via the double-depth experience replay system. The findings of a simulation experiment show that the agent can efficiently learn the most optimal radar interference suppression method by knowing that the interference suppression algorithm library contains algorithms that can deal with current environmental interference signals. Furthermore, compared to the PER–double deep Q-Network (PER-DDQN) presented by Zhang, the average accuracy, speed, and stability of decision-making are respectively increased by 6.4%, 2.51%, and 102.12%.