A Deep Q-Network Based Intelligent Decision-Making Approach for Cognitive Radar

Yong TIAN, Peng WANG, Xinyue HOU, Junpeng YU, Xiaoyan PENG, Hongshu LIAO, Lin GAO

Summary :

The electromagnetic environment is increasingly complex and changeable, and radar needs to meet the execution requirements of various tasks. Modern radars should improve their intelligence level and have the ability to learn independently in dynamic countermeasures. It can make the radar countermeasure strategy change from the traditional fixed anti-interference strategy to dynamically and independently implementing an efficient anti-interference strategy. Aiming at the performance optimization of target tracking in the scene where multiple signals coexist, we propose a countermeasure method of cognitive radar based on a deep Q-learning network. In this paper, we analyze the tracking performance of this method and the Markov Decision Process under the triangular frequency sweeping interference, respectively. The simulation results show that reinforcement learning has substantial autonomy and adaptability for solving such problems.

• Publication: IEICE TRANSACTIONS on Fundamentals Vol.E105-A No.4 pp.719-726
• Publication Date: 2022/04/01
• Publicized: 2021/10/15
• Online ISSN: 1745-1337
• DOI: 10.1587/transfun.2021EAP1072
• Type of Manuscript: PAPER
• Category: Neural Networks and Bioengineering

Authors

Yong TIAN
  University of Electronic Science and Technology of China
Peng WANG
  University of Electronic Science and Technology of China
Xinyue HOU
  University of Electronic Science and Technology of China
Junpeng YU
  University of Electronic Science and Technology of China
Xiaoyan PENG
  University of Electronic Science and Technology of China
Hongshu LIAO
  University of Electronic Science and Technology of China
Lin GAO
  University of Electronic Science and Technology of China

Keyword

cognitive radar,  deep Q-learning network,  electronic countermeasures,  Markov decision process