摘要
受到战争等特殊环境下部分节点导航拒止、节点移动性与环境干扰所带来的影响,快速进行测控网络拓扑重构是保证连续测控关键。为了解决上述问题,针对多体制无人集群测控网络的场景,提出一种基于多智能体深度确定性策略梯度(multi-agent deep deterministic policy gradient,MADDPG)的分布式多智能体测控网络群切换算法。该算法运用局部可观测马尔可夫决策模型,并考虑最小连通度、能耗与测控精度设计奖励函数,构建可靠的测控定位系统。仿真结果表明,该算法在不同的干扰环境下能有效抵抗外界干扰,保证测控定位的正常运行,与传统切换算法相比切换成功率提升12%以上。
Affected by the navigation denial in special environments such as war,node mobility and environmental interference,rapid topology reconfiguration of the measurement and control network is the key to ensure continuous positioning.In order to solve the above problems,a distributed multi-agent measurement and control network group switching algorithm based on multi-agent deep deterministic policy gradient(MADDPG)for the scenario of multi system unmanned cluster measurement and control network is proposed.This algorithm employs a partially observable Markov decision process model and considers minimum connectivity,energy consumption and measurement and control precision to design the reward function,and constructs a reliable measurement and control localization system.Simulation results show that the algorithm is feasible and effective in different interference environments to effectively resist external interference,ensure the normal operation of measurement,control and localization,and improve the switching success rate to more than 12%compared with traditional switching algorithms.
作者
王慧琳
刘胜利
谢岸宏
朱立东
WANG Huilin;LIU Shengli;XIE Anhong;ZHU Lidong(National Key Laboratory of Wireless Communications,University of Electronic Science and Technology of China,Chengdu 611731,China;Beijing Institute of Tracking and Communication Technology,Beijing 100094,China;The 10th Research Institude of China Electronics Technology Group Cooperation,Chengdu 610036,China)
出处
《系统工程与电子技术》
北大核心
2025年第6期2036-2046,共11页
Systems Engineering and Electronics
基金
国家自然科学基金(62371098)资助课题。
关键词
拓扑重构
群切换
测控定位
局部可观测马尔可夫决策
最小连通度
topology reconfiguration
group switching
measurement and control positioning
partially observable Markov decision
minimum connectivity
