上海航天（中英文）2026，Vol.43Issue(1)：82-90,9.DOI:10.19328/j.cnki.2096-8655.2026.01.008
一种基于近端策略优化的认知成像雷达抗有源干扰策略生成方法
A Proximal Policy Optimization-based Anti-jamming Strategy Generation Method for Cognitive Imaging Radar
摘要
Abstract
Synthetic aperture radar(SAR)plays a vital role in surface imaging of terrestrial and maritime environments.However,with the increasing complexity of the electromagnetic environment,SAR systems are vulnerable to various forms of active jamming,which severely degrade the imaging performance of SAR.To enhance the anti-jamming capability of SAR,effective scheduling of transmission resources is essential.To address the anti-jamming problem under complex and diverse jamming scenarios,in this paper,a proximal policy optimization(PPO)-based anti-jamming strategy generation method for radar is proposed.An anti-jamming model for SAR is established,and a policy gradient-based optimization framework is developed.By flattening the state and action spaces and carefully designing the reward function,the proposed method effectively mitigates the challenges of slow policy generation and convergence to local optima in high-dimensional radar decision spaces.The simulation results demonstrate that,compared with the dueling double deep Q-network(D3QN),the proposed approach significantly accelerates the policy generation under combined jamming conditions,particularly in high-dimensional transmission parameter decision spaces,with the optimal number of pulses increased by 2.86 times.关键词
合成孔径雷达(SAR)/组合干扰/抗干扰决策/强化学习/近端策略优化(PPO)Key words
synthetic aperture radar(SAR)/composite jamming/anti-jamming decision-making/reinforcement learning/proximal policy optimization(PPO)分类
信息技术与安全科学引用本文复制引用
孔祥磊,安洪阳,张驰,杨海光,冉瑞林,李中余,武俊杰,杨建宇..一种基于近端策略优化的认知成像雷达抗有源干扰策略生成方法[J].上海航天（中英文）,2026,43(1):82-90,9.基金项目
雷达探测感知全国重点实验室开放基金资助项目(2401074240410) （2401074240410）
