摘要
针对无人机集群分配模型依赖经验性军事价值评估、难以适应动态战场的问题,提出了一种基于熵权优劣解距离(TOPSIS)的目标军事价值评估算法,并结合静态属性综合评估体系,对目标价值进行综合评判。针对路径成本估计中忽略环境和集群约束的局限,设计了一种基于分层双向A*的路径规划算法,能够快速生成可行路径,显著提升了路径规划速度。为解决粒子群算法在约束优化中易早熟的缺陷,提出了基于非线性惯性权重的改进粒子群算法(SIPSO),通过Sigmoid函数的权重衰减机制,在全局与局部搜索间取得平衡。实验结果验证了SIPSO算法在小规模和大规模战场仿真中的优越性,证明了该算法在无人机集群对地打击任务中的合理性与高效性。
To address the limitations of unmanned aerial vehicle swarm allocation models that rely on empirical assessments and lack adaptability to dynamic environments,a target military value evaluation method based on entropy-weighted TOPSIS is proposed,incorporating static attribute analysis for comprehensive assessment.To overcome the inadequacy limitations of Euclidean distance in path cost estimation,a hierarchical bidirectional A*algorithm is developed,which leverages prior battlefield maps to rapidly generate feasible paths.Experiments show a tenfold improvement in planning speed,with minimal sacrifice in optimality.Additionally,a Sigmoid-based inertia weight strategy is introduced into Particle Swarm Optimization(SIPSO)to mitigate premature convergence.The improved algorithm achieves better global-local search balance and demonstrates superior solution quality and convergence in both small-and large-scale battlefield simulations.These results validate the proposed method’s effectiveness and efficiency in target assignment for unmanned swarm ground strike missions.
作者
丁家炜
张如飞
王志胜
DING Jiawei;ZHANG Rufei;WANG Zhisheng(College of Automation,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China;Beijing Institute of Control and Electronics Technology,Beijing 100038,China)
出处
《机械与电子》
2025年第12期59-67,共9页
Machinery & Electronics
关键词
协同打击
无人机集群
任务分配
目标属性
coordinated attack
unmanned cluster
task allocation
target attribute
