基于粒子群优化的多导弹动态武器目标分配算法  

Multi-missile Dynamic Weapon Target Assignment Algorithm Based on Particle Swarm Optimization

作　　者：刘攀 徐胜利 张迪 甄子洋[1] LIU Pan;XU Shengli;ZHANG Di;ZHEN Ziyang(College of Automation Engineering,Nanjing University of Aeronautics&Astronautics,Nanjing 211106,China;Shanghai Electro‑Mechanical Engineering Institute,Shanghai 201109,China)

机构地区：[1]南京航空航天大学自动化学院,南京211106 [2]上海机电工程研究所,上海201109

出　　处：《南京航空航天大学学报》2023年第1期108-115,共8页Journal of Nanjing University of Aeronautics & Astronautics

基　　金：国家自然科学基金(61973158);南京航空航天大学前瞻布局科研专项基金(1003-ILA22064);国防基础科研项目(JCKY2018203C020)。

摘　　要：针对复杂多变的未来战场环境,对空防御系统需要实现对多个目标进行武器分配。由于传统静态武器目标分配(Static weapon target assignment,SWTA)模型受到很多因素的限制,无法适应战场态势的快速变化。为了解决多导弹的动态武器目标分配(Dynamic weapon target assignment,DWTA)问题,将对空防御过程离散为多个阶段,并根据战场实时态势数据构建了DWTA的数学模型,提出了一种改进的粒子群优化算法,引入了武器转火时间窗等约束条件,在算法中考虑拦截概率和导弹耗费等多个指标。最后通过大量仿真实验,验证了粒子群算法进行多导弹目标分配的合理性和有效性。In view of the complex and changeable future battlefield environment,the air defense system needs to realize the assignment of weapons to multiple targets.Because the traditional static weapon target assignment(SWTA)model is restricted by many factors,it cannot adapt to the rapid changes in the battlefield situation.In order to solve the problem of multi-missile dynamic weapon target assignment(DWTA),the air defense process is discretized into multiple stages,and a mathematical model of DWTA is constructed based on the real-time situation data of the battlefield,and an improved particle swarm optimization algorithm is proposed.Constraints such as the time window for the weapon to fire are considered,and multiple indicators such as interception probability and missile cost are considered in the algorithm.Finally,through a large number of simulation experiments,the rationality and effectiveness of particle swarm algorithm for multi-missile target assignment are verified.

关 键 词：粒子群算法 态势评估 空战对抗 武器目标分配 动态分配 

分 类 号：E91[军事]