主被动协同定位空能资源优化配置方法OA北大核心CSTPCD

The rapid development of UAVs has brought great convenience to today′s society,but their potential misuse poses a risk to public safety.As a result,in recent years,surveillance and localization technologies for UAVs have been widely studied.In response to the application problem of difficulty in accurate localization of long-range low-flying UAVs,a cooperative localization framework is proposed,mainly for passive localization,and it is supplemented by active detection.Based on the passive localization using the time difference of arrival(TDOA),the active detection equipment supporting round-trip time of arrival(RT-TOA)measurement is introduced to locate the UAVs opportunistically and actively.These devices compensate for the missing target elevation information of passive localization,to improve the three-dimensional localization accuracy of UAVs.This paper delves into the spatial and power sources allocation methods for active localization nodes under the pre-deployment of passive localization nodes.Under the framework of cooperative localization,it derives the localization accuracy measurement indicator and formulates the joint optimization problem for spatial and power resources.A resource optimization algorithm for improved gray wolf optimization based on nonlinear convergence factors and memory guidance(CM-IGWO)is proposed.Simulation results show that the active and passive cooperative localization effect is better than the passive localization effect,and that the elevation localization accuracy in typical scenarios is significantly improved by 96.33% .In addition,the proposed CM-IGWO algorithm is superior to the gray wolf optimization(GWO)and IGWO when solving the joint optimization problem for spatial and power resources.