An Intelligent Anti-jamming Decision-making Method Based on Deep Reinforcement Learning for Cognitive Radar

Abstract

Due to the rapid development of cognitive radar and the complicated electromagnetic environment, traditional anti-jamming decision-making methods are no longer suitable to modern electronic counter-countermeasures. Reinforcement learning brings a novel solution to this problem. In this paper, a method based on deep reinforcement learning is applied in the anti-jamming decision-making system of cognitive radar. We construct the environment model for cognitive radar and propose a modified deep deterministic policy gradient algorithm for decision-making. The experimental results demonstrate that the proposed method is effective in the application of anti-jamming decision-making system of cognitive radar. Furthermore, the performance analysis shows that the proposed algorithm converges faster than other classical algorithms and more suitable to high-dimensional state and action space problems.