An Intelligent Anti-jamming Decision-making Method Based on Deep Reinforcement Learning for Cognitive Radar
Wen Jiang, Yanping Wang, Yang Li, Yun Lin, Wenjie Shen
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.