基于深度Q学习的组网雷达闪烁探测调度方法

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中图分类号： TN973 文献标志码：A DOI：10.12305/j.issn.1001-506X.2025.05.07

Abstract：The neted radar scintilation detection system can improve the cooperative detection performance and survival rate of radar.It is an urgent problem to select a suitableradar cooperative detection startup and limit the startup exposure time of a single radar to adapt to the ever-changing environmental threats. In this regard，a netted radar scintilltion detection scheduling method is presented based on deep Q-learning（DQL） reinforcement learning algorithmto limit the startup time of a single radar.Firstly，the threat degree model of theair jammer tothe netted radar and the scintillation detection modelof the netted radar to the air jammer are established.Then，thereinforcement learning reward functionof the threat degree and the neted scintilation detection probability is proposed.Finally，the optimal scintilation startup decision scheduling scheme of the netted radar is obtained by using the proposed DQL algorithm.The simulation results show that the average benefit rate of the proposed DQL scheduling method is superior to random scheduling，artificial bee colony scheduling and double deep Q network（DDQN） scheduling methods，and the scheduling response time is less.

Keywords：netted radar；scintillation detection；reinforcement learning；deep Q -learning（DQL）；double deep Q network（DDQN）

0 引言

空中干扰机执行突防任务时搭载了机载电子干扰设备，在干扰突防过程中，空中干扰机会对地面雷达网实施电磁干扰，伺机收集地面雷达信息，单部雷达很难完成探测和抗干扰任务，也很难保证探测结果的可靠性和准确性。（剩余13551字）