摘要
除去测量误差、算法误差、电磁环境因素,无源定位的精度也受到侦察站与目标相对位置关系的影响,为解决多侦察目标下无人机蜂群定位布战问题,提出了一种将大粒子分割为小粒子的改进粒子群算法。改进算法使一个粒子群能够并行处理适应度函数相同的多个问题,并设置各类策略使粒子具有跳出局部最优的能力。通过与传统粒子群算法及各类传统布站方式进行比较,验证了改进算法对无人机蜂群布站方式寻优的可行性与优越性,在存在多个侦察目标的情况下可以通过该算法合理分配蜂群无人机的位置来进行侦察布站任务。
In order to solve the problem of positioning and distribution of UAV colony under multi reconnaissance targets,a new algorithm is proposed to solve this problem.The improved algorithm enables a particle swarm to deal with multiple problems with the same fitness function in parallel,and sets various strategies to make the particles have the ability to jump out of the local optimum.Compared with the traditional particle swarm optimization algorithm and various traditional station layout methods,the feasibility and superiority of the improved algorithm in optimizing the drone swarm station layout method is verified.In the case of multiple reconnaissance targets,the location of the drone swarm can be reasonably allocated by this algorithm to carry out the reconnaissance station layout task.
作者
王钊
王华兵
朱明威
刘高高
WANG Zhao;WANG Hua-bing;ZHU Ming-wei;LIU Gao-gao(State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System(CEMEE),Luoyang Henan 471003,China;Xidian University,Xi'an Shaaxi 710071,China)
出处
《计算机仿真》
北大核心
2023年第3期64-70,251,共8页
Computer Simulation
基金
陕西省自然科学基础研究计划(2021JQ-210)
中央高校基本科研业务费专项资金(XJS200216,JB210202)。
关键词
无人机蜂群
无源定位
侦察布站
粒子群算法
Drone colony
Passive location
Distribution of reconnaissance stations
Particle swarm optimization(PSO)
