对多功能雷达的DQN认知干扰决策方法

基于Q-Learning的认知干扰决策方法随着多功能雷达(multifunctional radar, MFR)可执行的任务越来越多,决策效率明显下降。对此,提出了一种对MFR的深度Q神经网络(deep Q network, DQN)干扰决策方法。首先,分析MFR信号特点并构建干扰库,以此为基础研究干扰决策方法。其次,通过对DQN原理的简要阐述,提出了干扰决策方法及其决策流程。最后,对该决策方法进行了仿真试验并通过对比DQN和Q-Learning的决策性能,验证了所提方法的必要性。为提高决策的实时性和准确率,对DQN算法进行了改进,在此基础上,结合先验知识进一步提高了决策效率。仿真试验表明:该决策方法能够较好地自主学习实际战场中的干扰效果,对可执行多种雷达任务的MFR完成干扰决策。

DQN based decision-making method of cognitive jamming against multifunctional radar

With the increasing number of tasks that can be performed by multifunctional radar (MFR), the decision-making efficiency of Q-Learning based decision-making methods of cognitive jamming is significantly reduced. Aiming at this, a deep Q neural network (DQN) based jamming decision-making method against MFR is proposed. Firstly, the characteristics of MFR signals are analyzed and the jamming library is constructed. Based on this, the jamming decision-making method is studied. Secondly, through the brief explanation of the DQN principle, the jamming decision-making method and its process are proposed. Finally, the simulation test of the decision-making method is carried out and the necessity of the method is verified by comparing the decision-making performance of DQN and Q-Learning. In order to improve the real-time and accuracy of decision-making, the DQN algorithm has been improved. On this basis, combined with prior knowledge, the decision-making efficiency is further improved. The simulation test shows that the decision-making method can learn the jamming effect in the actual battlefield autonomously, and complete the decision-making of cognitive jamming against the MFR that can perform multiple radar tasks.