基于改进PGA-PSO的多无人机协同雷达侦察任务分配

Multi-UAVs Cooperative Radar Reconnaissance Task Assignment Based on Improved PGA-PSO

文章研究多无人机协同侦察相控阵雷达模式转移规律过程中的任务分配优化。为了以最小能耗无重复侦察雷达全部波位,根据相控阵雷达波位覆盖方式,分析比较了三种波位侦察点选择方案。在此基础上,根据侦察点访问约束、无人机航程和能耗约束,以无人机总能耗最低为目标,建立多无人机侦察任务分配的多旅行商优化模型。基于单亲遗传一粒子群混合算法(PGA-PSO),使用分组最优个体选择和多变异操作,保证算法快速收敛的同时,提高算法求解精度。仿真结果表明,通过优选侦察点和优化任务分配可大幅提高多无人机协同的侦察效率,及进的PGA-PSO算法较其他算法求解精度高,收敛速度快。

This paper studies the optimization of task assignment in the process of multi-UAVs reconnoitring the mode transfer rule of a phased array radar cooperatively.According to the beam position coverage method of a phased array radar,three schemes of beam position reconnaissance point selection are analyzed and compared in order to reconnoitre entire beam positions of the radar with minimum energy consumption without repetition.On this basis,based on the access constraints of reconnaissance points,UAV range and energy consumption constraints,a multi-traveling salesmen optimization model for multi-UAVs reconnaissance task assignment is established with the goal of minimizing the total energy consumption of UAVs.Based on partheno genetic-particle swarm optimization algorithm(PGA-PSO),grouping optimal individual selection and multiple mutation operation are used to ensure the fast convergence of the algorithm and improve the accuracy of the algorithm.The simulation results show that the efficiency of multi-UAVs cooperative reconnaissance can be greatly improved by optimizing reconnaissance points and task assignment.The improved PGA-PSO algorithm has higher solution accuracy and faster convergence speed than other algorithms.