摘要
合成孔径雷达(SAR)在对地、海面成像中占据了重要地位,然而,随着电磁环境的日益复杂,SAR会受到多样式有源干扰的影响,严重制约SAR成像效能。通过对SAR发射资源的合理调度,可提升SAR的主动抗干扰能力。本文针对多样组合干扰条件下的抗干扰问题,提出一种基于近端策略优化的雷达抗干扰策略生成方法。首先建立SAR抗干扰模型,提出基于策略梯度的优化方法,通过将状态空间与动作空间扁平化和奖励函数的设计,解决了雷达高维决策空间下策略生成慢、容易收敛到局部最优的问题。仿真结果表明:与双重深度双Q网络相比,显著提高了组合干扰样式下雷达多维发射参数高维决策空间下的策略生成速度,最佳脉冲数提升了2.86倍。
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 antijamming 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.
作者
孔祥磊
安洪阳
张驰
杨海光
冉瑞林
李中余
武俊杰
杨建宇
KONG Xianglei;AN Hongyang;ZHANG Chi;YANG Haiguang;RAN Ruilin;LI Zhongyu;WU Junjie;YANG Jianyu(School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu 611731,Sichuan,China)
出处
《上海航天(中英文)》
2026年第1期82-90,共9页
Aerospace Shanghai(Chinese&English)
基金
雷达探测感知全国重点实验室开放基金资助项目(2401074240410)。
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)
synthetic aperture radar(SAR)
composite jamming
anti-jamming decision-making
reinforcement learning
proximal policy optimization(PPO)
