近距空中支援中无人机目标分配问题研究  

Research on Target Allocation of Multiple UAV in CAS

作　　者：吴立冬 李宗璞[1] 彭岳松 熊子涵 WU Lidong;LI Zongpu;PENG Yuesong;XIONG Zihan(Equipment Management and Unmanned Aerial Vehicle Engineering School,Air Force Engineering University,Xi’an710051,China;Air Traffic Control and Navigation School,Air Force Engineering University,Xi’an710051,China;Air Defense and Antimissile School,Air Force Engineering University,Xi’an 710051,China)

机构地区：[1]空军工程大学装备管理与无人机工程学院,西安710051 [2]空军工程大学空管领航学院,西安710051 [3]空军工程大学防空反导学院,西安710051

出　　处：《空军工程大学学报》2024年第3期86-93,共8页Journal of Air Force Engineering University

摘　　要：利用无人机实施近距空中支援作战是当前研究的重点,需针对复杂战场环境对实行多目标火力打击的无人机进行路径优化与目标分配。综合考虑无人机飞行高度、不同武器挂载、目标类型及各项指标、威胁区威胁代价、作战时间代价等因素,构建目标毁伤综合指标提高非对称打击效能,设置威胁区安全阈值提高路径有效性分析,采取自适应权重粒子群协同优化算法求解单无人机单目标航迹总代价,采取0-1规划法求解多无人机对多目标的不平衡任务分配方案,为近距空中支援任务中多无人机对多目标的分配提供决策支撑。Operation utilized by air support of drones at a close range is currently a research focus.The path and target allocation of Unmanned Aerial Vehicles(UAVs)for multi target firepower strikes require optimization under conditions of complex battlefield environments,and the following factors,such as UAV flight altitude,different weapon mounts,target types and various indicators,threat cost in threat zones,and operational time cost,should be taken into account.A comprehensive target damage index is constructed to improve asymmetric strike effectiveness,and a threat zone security threshold is set to improve path effectiveness analysis.The total cost of the optimal trajectory is planned by adopting the Adaptive Weighted Particle Swarm Collaborative Optimization(APSCO)algorithm for a single drone and a single target,and the imbalanced task allocation scheme is completed by adopting the 0-1 programming method for multiple drones against multiple targets,providing decision support for the allocation of multiple unmanned aerial vehicles to multiple targets at a close range in performing air support tasks.

关 键 词：近距空中支援 多无人机 多目标 目标分配 自适应权重粒子群协同优化算法 

分 类 号：V279[航空宇航科学与技术—飞行器设计] E91[军事]