基于可拦截因子和HGAPSO算法的火力分配模型研究  

Research on a Firepower Allocation Model Based on the Interceptable Factor and HGAPSO Algorithm

作　　者：程文铮 张鹏飞[2] 张润哲 米江勇 张宸睿 CHENG Wenzheng;ZHANG Pengfei;ZHANG Runzhe;MI Jiangyong;ZHANG Chenrui(College of Mechatronics Engineering,North University of China,Taiyuan 030051,Shanxi,China;Research Institute of Intelligent Weapons,North University of China,Taiyuan 030051,Shanxi,China;College of Automotive Engineering,Shanxi Vocational University of Engineering Science and Technology,Jinzhong 030606,Shanxi,China)

机构地区：[1]中北大学机电工程学院,山西太原030051 [2]中北大学智能武器研究院,山西太原030051 [3]山西工程科技职业大学汽车工程学院,山西晋中030606

出　　处：《火炮发射与控制学报》2024年第3期17-24,30,共9页Journal of Gun Launch & Control

基　　金：山西省基础研究计划资助项目(202103021224182);山西省基础研究计划资助项目(202103021224187);山西省研究生实践创新项目(2023SJ190)。

摘　　要：火力分配作为集群目标来袭防御任务规划的关键环节,对提高防御效果具有重要意义。针对高炮反无人机的火力分配问题,将高炮性能指标约束和转火时间约束转化为可拦截因子,提出一种基于可拦截因子的高炮反无人机火力分配模型,减小非线性约束转化为惩罚函数带来的计算量及误差,进而提升整体效能。基于此模型,针对来袭目标与火力节点之间的火力优化匹配问题,采用改进混合遗传粒子群算法(Hybrid GA and PSO,HGAPSO)优化算法对模型进行最优值求解。仿真试验结果表明该模型合理有效,HGAPSO算法有较高的收敛精度和较快的收敛速率。As a key link in the task planning of counter-swarm defense,firepower allocation plays an important role in improving defense effect.Aiming at the problem of firepower allocation in the case of antiaircraft guns against UAVs,a fire allocation model based on the interceptable factor such as the constraints of anti-aircraft gun performance indices and fire shift time is proposed,which reduces the computation and error caused by the nonlinear constraints of the penalty function,and improves overall efficiency.Based on this model,the HGAPSO optimization algorithm is used to find the optimal value of the model for the firepower optimization matching problem of the incoming target and the firepower node.The simulation results show that the model is scientific,reasonable and effective,and the HGAPSO algorithm has higher convergence accuracy and faster convergence rate.

关 键 词：反无人机 火力分配模型 可拦截因子 混合遗传粒子群算法 

分 类 号：TJ35[兵器科学与技术—火炮、自动武器与弹药工程]