Systems Engineering and Electronics ›› 2018, Vol. 40 ›› Issue (5): 1031-1035.doi: 10.3969/j.issn.1001-506X.2018.05.11

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Intelligent radar countermeasure based on Q-learning

XING Qiang1, JIA Xin2, ZHU Weigang2   

  1. 1. Department of Graduate Management, Space Engineering University, Beijing 101416, China; 2. Department of Electronic and Optical Engineering, Space Engineering University, Beijing 101416, China
  • Online:2018-04-28 Published:2018-04-24

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Abstract:

With the progress of radar technology, the development of radar tends to multifunction and intellectualization. The anti-jamming capability of radar is enhanced, and the combat effectiveness of the radar countermeasure method for conventional radar is decreasing. The countermeasure method for multifunction radar, especially for a number of working modes unknown, has become the hotspot and difficulty of research. Based on this, this paper expounds the intelligent radar countermeasure (IRC) method, and compares the difference between IRC and traditional radar countermeasure (TRC). The basic principle of reinforcement learning (RL) is introduced. For the situation with a number of working modes unknown, IRC based on Q-learning is proposed, and the algorithm steps are given. The relationship between the convergence time, the convergence value and the cycle times of the  Q matrix is analyzed. Simulation results show that the Q  matrix convergence time is only the second magnitude under the given simulation experiment condition. The radar countermeasure system can learn independently and make decision according to the jamming effect, which improves the real-time performance and adaptability, and can resist the multi-working mode radar simultaneously.

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