基于强化学习的雷达干扰决策技术综述

A Review on Reinforcement Learning Based Radar Jamming Decision-Making Technology

装备的认知能力集中体现了认知电子战的根本属性,也是能否对复杂、智能的电子装备实施有效干扰的关键。强化学习作为人工智能领域炙手可热的技术手段,具备不依赖先验数据的自学习能力,是解决多功能雷达干扰难题的一个重要途径。在回顾传统的雷达干扰决策算法的基础上,阐述了利用强化学习进行雷达干扰决策的原理,分析了基于强化学习的雷达干扰决策技术的发展现状,并通过仿真分析了基于强化学习的干扰决策方法的性能,最后进行了总结与展望。

The cognitive ability of equipment embodies the fundamental attributes of cognitive electronic warfare,and it is also the key to effective jamming on complex and intelligent electronic equipment.As a hot technique in the field of artificial intelligence,reinforcement learning has the ability of self-learning not relying on priori data,which is an important approach to multifunctional radar jamming.Based on the review on traditional algorithms of radar jamming decision-making,as for reinforcement learning based radar jamming decision-making,the principles and status quo of the technology are analyzed,and its performance is verified through simulations.Finally,a summary is given,and the outlook for the technology is predicted.