基于动态贝叶斯网络的多无人机集群对抗策略  

Research on the multiple unmanned aerial vehicle swarm confrontation strategy based on the dynamic Bayesian network

作　　者：贾永楠 焦宇航 陈萱 李擎[1,2] 鲁小雅 JIA Yongnan;JIAO Yuhang;CHEN Xuan;LI Qing;LU Xiaoya(School of Automation and Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,China;Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education,University of Science and Technology Beijing,Beijing 100083,China)

机构地区：[1]北京科技大学自动化学院,北京100083 [2]工业过程知识自动化教育部重点实验室,北京100083

出　　处：《工程科学学报》2024年第7期1216-1226,共11页Chinese Journal of Engineering

基　　金：国家自然科学基金面上资助项目(62273033);科技部外专项目(北科大专字G2022105022L)。

摘　　要：红蓝双方集群攻防对抗博弈问题是近年来复杂系统研究领域的热点和难点,在军事领域、网络安全领域和人工智能领域均具有重要的应用价值.在实际对抗中,环境的不确定性和智能体行为的多样性导致问题难以建模,而实战环境又要求智能体能够对态势的变化给出实时、高效的响应.为解决上述问题,本文提出了一种面向红蓝双方集群攻防对抗博弈问题的研究框架.首先,提出了一种基于改进后的兰彻斯特方程的对抗博弈模型,并在此基础上探讨了如何适应性改进Kuhn–Munkres(KM)算法以解决对抗博弈过程中的多目标任务分配问题.其次,为了提升无人机个体的环境适应性,提出了一种集群攻防对抗策略,利用动态贝叶斯网络对集群攻防对抗过程中产生的一系列不确定性因素进行实时、高效的推理和预测.该策略可有效降低对抗模型的复杂度和计算量,广泛提高决策的精确性和快速性.最后,基于上述对抗博弈模型搭建了仿真平台,实时展示红蓝双方无人机集群对抗过程,并对上述算法的有效性进行验证.仿真结果表明,所提出的上述理论框架可以实现红蓝双方对抗模拟演示过程,可有效解决红蓝双方打击对抗过程中的多目标任务分配问题,并对对抗过程中所产生的不确定性因素进行合理的预测和评估.The swarming confrontation problem of unmanned aerial vehicles(UAVs)has been a focal point and challenge in the field of complex systems research in recent years,with significant application value in the military,network security,and artificial intelligence industries.In real-world confrontations,the uncertainty of the environment and the diversity of intelligent agent behaviors render the problem difficult to model,and operational environments necessitate intelligent agents to provide real-time,efficient responses to changes in the situation.To address these challenges,this paper proposes a research framework for the swarming confrontation problem of UAVs.First,an adversarial game model is developed based on the improved Lanchester equation to solve this problem toward the red and blue swarms.The adversarial game model focuses on describing the dynamic quantity change process of the red and blue UAV swarms.Second,based on the above confrontation model,a multiple-task assignment problem is investigated,which is derived from the above confrontation process.A new assignment strategy of these UAVs for strike tasks is proposed by adaptive improvement based on the traditional Kuhn–Munkres algorithm.This strategy is suitable for the red and blue parties under the adversarial environment,which can effectively complete the strike tasks and improve the confrontation ability of these UAVs.Third,a swarming confrontation algorithm is proposed to improve the environmental suitability of each UAV,especially when dealing with the influences of a series of uncertain factors generated by the real-time process of swarming offensive and defensive confrontations.This algorithm is based on the dynamic Bayesian network structure and focuses on predicting and evaluating the uncertainty generated during the confrontation process of the red and blue UAV swarms and performing corresponding reasoning and prediction through the dynamic Bayesian network,which can effectively reduce the complexity and calculation of the confrontation model

关 键 词：集群对抗 分布式协同 兰彻斯特方程 KM算法 动态贝叶斯网络 

分 类 号：V249.1[航空宇航科学与技术—飞行器设计]