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Acta Armamentarii ›› 2023, Vol. 44 ›› Issue (5): 1422-1430.doi: 10.12382/bgxb.2022.0069

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Anti-jamming Waveform Design of Ground-based Air Surveillance Radar Based on Reinforcement Learning

ZHENG Zexin, LI Wei*(), ZOU Kun, LI Yanfu   

  1. Information and Navigation School, Air Force Engineering University,Xi’an 710077, Shaanxi, China
  • Received:2022-01-28 Online:2022-07-21
  • Contact: LI Wei

Abstract:

The electronic warfare capability of the detected target seriously affects the detection and identification performance of the ground-based air surveillance radar. To address the anti-jamming problem of the air surveillance radar, an anti-jamming waveform design method based on reinforcement learning is proposed. From the perspective of game theory, the reinforcement learning method is used to establish a dynamic confrontation model between radar and target jamming, calculate the state and action value of all parties in the game. The strategy iteration method is employed to generate the optimal strategy, and the time domain waveform is synthesized based on the phase iteration method. The simulation results shows that: when using the radar transmitted signal designed by the proposed method, compared with linear frequency-modulated signals and frequency-agile signals, the signal-to-interference-noise ratio is increased by 6.39dB and 1.12dB respectively; when the total signal power is 11W, compared with the frequency-agile signal, the target detection probability is increased by 5%, meaning that the designed signal can improve the anti-interception performance of radar signals while achieving anti-jamming at low power.

Key words: ground-based air surveillance radar, waveform design, reinforcement learning, anti-jamming