An Intelligent Anti-jamming Decision-making Method Based on Deep Reinforcement Learning for Cognitive Radar
- Resource Type
- Conference
- Authors
- Jiang, Wen; Wang, Yanping; Li, Yang; Lin, Yun; Shen, Wenjie
- Source
- 2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD) Computer Supported Cooperative Work in Design (CSCWD), 2023 26th International Conference on. :1662-1666 May, 2023
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Nuclear Engineering
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Deep learning
Federated learning
Spaceborne radar
Decision making
Signal processing algorithms
Reinforcement learning
Signal processing
Cognitive radar
anti-jamming decision-making
reinforcement learning
deep reinforcement learning
radar signal processing
- Language
- ISSN
- 2768-1904
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.