基于先验知识的多功能雷达智能干扰决策方法  

Multi-function radar intelligent jamming decision method based on prior knowledge

作　　者：朱霸坤 朱卫纲 李伟 杨莹 高天昊 ZHU Bakun;ZHU Weigang;LI Wei;YANG Ying;GAO Tianhao(Department of Electronic and Optical Engineering,Space Engineering University,Beijing 101416,China;State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System,Luoyang 471032,China;Campany of Postgraduate Management,Space Engineering University,Beijing 101416,China)

机构地区：[1]航天工程大学电子光学工程系,北京101416 [2]电子信息系统复杂电磁环境效应国家重点实验室,河南洛阳471032 [3]航天工程大学研究生院,北京101416

出　　处：《系统工程与电子技术》2022年第12期3685-3695,共11页Systems Engineering and Electronics

基　　金：电子信息系统复杂电磁环境效应国家重点实验室项目(CEMEE2020Z0203B)资助课题。

摘　　要：针对基于强化学习的多功能雷达干扰决策方法训练周期长、收敛慢的问题,本文提出了基于先验知识的多功能雷达智能干扰决策算法。所提算法使用了基于势能函数的收益塑造理论,利用先验知识设置收益函数,相比于传统算法,具有更快的收敛速率。利用先验知识加速算法收敛速率的方法对强化学习在多功能雷达干扰决策中的实际应用具有重要的意义,对于强化学习在其他领域的应用也具有很好的参考价值。In view of the problems of long training period and slow convergence of multi-function radar jamming decision method based on reinforcement learning,this paper proposes a multi-function radar intelligent jamming decision algorithm based on prior knowledge.The proposed algorithm uses the revenue shaping theory based on potential function,and uses prior knowledge to set the revenue function.Compared with the traditional algorithm,the algorithm has faster convergence rate.The method of accelerating the convergence rate of algorithm by using prior knowledge is of great significance for the practical application of reinforcement learning in multi-function radar jamming decision,and also has a good reference value for the application of reinforcement learning in other fields.

关 键 词：雷达对抗 马尔可夫决策过程 强化学习 收益塑造 先验知识 

分 类 号：TN974[电子电信—信号与信息处理]