面向飞行目标的多传感器协同探测资源调度方法  

Resource Scheduling Method of Multi-sensor Cooperative Detection for Flying Targets

作　　者：汪梦倩 梁皓星 郭茂耘[1] 陈小龙 武艺 WANG Meng-Qian;LIANG Hao-Xing;GUO Mao-Yun;CHEN Xiao-Long;WU Yi(School of Automation,Chongqing University,Chongqing 400044)

机构地区：[1]重庆大学自动化学院,重庆400044

出　　处：《自动化学报》2023年第6期1242-1255,共14页Acta Automatica Sinica

摘　　要：针对飞行目标机动性带来的多传感器协同探测资源调度动态性需求,提出一种新的基于近端策略优化(Proximal policy optimization,PPO)与全连接神经网络结合的多传感器协同探测资源调度算法.首先,分析影响多传感器协同探测资源调度的复杂约束条件,形成评价多传感器协同探测资源调度过程指标;然后,引入马尔科夫决策过程(Markov decision process,MDP)模拟多传感器协同探测资源调度过程,并为提高算法稳定性,将Adam算法与学习率衰减算法结合,控制学习率调整步长;最后,基于改进近端策略优化与全卷积神经网络结合算法求解动态资源调度策略,并通过对比实验表明该算法的优越性.Aiming at the dynamic demand of multi-sensor cooperative detection resource scheduling brought by the maneuverability of flying targets,a new multi-sensor cooperative detection resource scheduling algorithm based on proximal policy optimization(PPO)and fully connected neural network is proposed.In this paper,we first build a constraint index model that affects the scheduling of multi-sensor cooperative detection resources.Next,we introduce the Markov decision process(MDP)to simulate the multi-sensor cooperative detection resource scheduling process,and in order to improve the stability of the algorithm,the Adam algorithm is combined with the learning rate attenuation algorithm to control the up-to-date step of learning rate.Finally,the optimal resource scheduling strategy is solved based on the improved proximal policy optimization and fully connected neural network algorithm,and the comparative experiment shows the superiority of the algorithm proposed in this paper.

关 键 词：多传感器协同 资源调度 马尔科夫决策过程 强化学习 

分 类 号：V35[航空宇航科学与技术—人机与环境工程] E926.4[军事—军事装备学] TP212[兵器科学与技术—武器系统与运用工程]