[1]丁家炜,张如飞,王志胜.考虑目标属性的无人机集群协同打击分配算法[J].机械与电子,2025,(12):59-67.
　DING Jiawei,ZHANG Rufei,WANG Zhisheng.An Unmanned Cluster Collaborative Strike Allocation Algorithm Considering Target Attributes[J].Machinery & Electronics,2025,(12):59-67.
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考虑目标属性的无人机集群协同打击分配算法()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2025年12期
页码:
59-67
栏目:
智能制造
出版日期:
2025-12-23

文章信息/Info

Title:
An Unmanned Cluster Collaborative Strike Allocation Algorithm Considering Target Attributes
文章编号:
1001-2257 ( 2025 ) 12-0059-09
作者:
丁家炜 1 ; 张如飞 2 ; 王志胜 1
1. 南京航空航天大学自动化学院,江苏 南京 211106 ;?
2. 北京控制与电子技术研究所,北京 100038
Author(s):
DING Jiawei1 ;  ZHANG Rufei2 ;  WANG Zhisheng1
( 1.College of Automation , Nanjing University of Aeronautics and Astronautics , Nanjing 211106 , China ;
2.Beijing Institute of Control and Electronics Technology , Beijing 100038 , China )
关键词:
协同打击; 无人机集群; 任务分配; 目标属性
Keywords:
coordinated attack ;  unmanned cluster ;  task allocation ;  target attribute
分类号:
TP18 ;V279
文献标志码:
A
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。
Abstract:
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.

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备注/Memo

备注/Memo:
收稿日期: 2025-07-22
作者简介:丁家炜 ( 2000- ),男,江苏靖江人,硕士研究生,研究方向为无人机集群决策与控制;张如飞 ( 1981- ),男,安徽怀远人,研究员,研究方向为导航制导与控制;王志胜 ( 1970- ),男,湖北荆门人,博士,教授,博士研究生导师,研究方向为智能机器人技术、智能感知与信息融合。
更新日期/Last Update: 2026-01-05