Volume 12 Issue 6
Dec.  2023
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Article Navigation >  Journal of Radars  > 2023  >  12(6): 1290-1304
XIE Feng, LIU Huanyu, HU Xikun, et al. A radar anti-jamming method under multi-jamming scenarios based on deep reinforcement learning in complex domains[J]. Journal of Radars, 2023, 12(6): 1290–1304. doi: 10.12000/JR23139
Citation: XIE Feng, LIU Huanyu, HU Xikun, et al. A radar anti-jamming method under multi-jamming scenarios based on deep reinforcement learning in complex domains[J]. Journal of Radars, 2023, 12(6): 1290–1304. doi: 10.12000/JR23139
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A Radar Anti-jamming Method under Multi-jamming Scenarios Based on Deep Reinforcement Learning in Complex Domains

DOI: 10.12000/JR23139 CSTR: 32380.14.JR23139
  • 1.

    Information Countermeasure Technique Institute, Faculty of Computing, Harbin Institute of Technology, Harbin 150080, China

  • 2.

    College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds:  The National Natural Science Foundation of China (62271166), Interdisciplinary Research Foundation of HIT (IR2021104)
More Information
  • Corresponding author: LIU Huanyu, liuhuanyu@hit.edu.cn
  • Received Date: 2023-07-31
  • Rev Recd Date: 2023-10-19
    • Available Online: 2023-10-24
  • Publish Date: 2023-11-09
    Abstract
  • In modern electronic warfare, the jamming environment of radar is more complex than ever. The airborne jammer adapts its jamming method based on diverse raid missions and stages. Recently, the reinforcement learning–based radar anti-jamming method has made some progress in the confrontation scenario of single jamming; however, the gap with respect to actual complex multi-jamming scenarios is large. To address this issue, this paper proposes a multi-jamming scenario radar anti-jamming method based on deep reinforcement learning in the complex domain to optimize the anti-jamming strategy of frequency agile radar. First, according to the stage characteristics of the raid mission, noise spot jamming, range deception jamming , and dense false target forwarding jamming models are established. The three jamming sequence strategies were designed to simulate actual jamming scenarios. Second, a reinforcement learning reward function that integrates the signal-to-noise ratio and target trajectory integrity is constructed for the multi-jamming scenario model. Thus, a multi-jamming scenario radar anti-jamming method based on deep reinforcement learning in a complex domain is proposed, which is based on the complex domain characteristics of the jamming signal. Finally, radar anti-jamming simulation experiments are performed based on the three jamming sequence strategies. The results show that the proposed method can effectively deal with the main-lobe jamming problem of complex multi-jamming scenarios under time-sequence conditions. Moreover, the average decision-making accuracy was improved, and the average decision-making time was reduced to 405.3 ms compared with the two classical reinforcement learning algorithms.

     

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