现代防御技术2025，Vol.53Issue(4)：10-17,8.DOI:10.3969/j.issn.1009-086x.2025.04.002
基于深度强化学习的传感器-武器-目标分配方法
Sensor-Weapon-Target Assignment Method Based on Deep Reinforcement Learning
闫世祥 1刘海军1
作者信息
- 1. 北京电子工程总体研究所,北京 100854
- 折叠
摘要
Abstract
Reasonable selection of combat resources to form a sensor-weapon-target kill-chains plays an important role in air defense network operations.This paper studies sensor-weapon-target assignment(S-W-TA)under multiple constraints and multiple optimization indexes,and proposes an deep reinforcement learning based on allocation method.The mathematical model of S-W-TA problem is established,and the concept of kill chain advantage is used to integrate the traditional efficiency index.The deep Q network(DQN)training agent is used to solve the S-W-TA problem by deep reinforcement learning method for the first time.The simulation results show that the solution obtained by the deep reinforcement learning algorithm is superior to the rule-based allocation method widely used in engineering,and the reinforcement learning algorithm is more suitable for solving the S-W-TA problem with multiple constraints and multiple optimization indexes,and has certain engineering application value.关键词
网络化作战/传感器-武器-目标分配/杀伤链/强化学习/深度Q网络Key words
networked operation/sensor-weapon-target assignment(S-W-TA)/kill-chain/reinforcement learning/deep Q network分类
军事科技引用本文复制引用
闫世祥,刘海军..基于深度强化学习的传感器-武器-目标分配方法[J].现代防御技术,2025,53(4):10-17,8.
