主被动协同定位空能资源优化配置方法

Algorithm for optimization of joint spatial and power resources for cooperative active and passive localization

无人机的快速发展为当今社会带来巨大便利,但其潜在的滥用现象对公共安全构成严重威胁,因此面向无人机的监测与定位技术近年来得到广泛研究。针对远距低飞无人机难以准确定位的应用问题,提出以无源为主、有源为辅的主被动协同定位框架,在基于到达时间差实现无源被动定位的基础上,引入支持往返到达时间测量的有源主动探测设备,择机对无人机进行主动式定位,补偿无源定位缺失的目标高程信息,从而提升无人机的三维定位精度。为充分挖掘主被动的协同定位潜力,文中深入探究无源被动定位节点预先部署的情况下,有源主动定位节点的空域和能域资源的配置方式,推导了主被动协同定位框架下的定位精度衡量指标,构建了空能资源联合优化问题,提出了基于非线性收敛因子和记忆指导的改进灰狼优化的空能资源优化算法。仿真结果表明,针对无人机定位时,主被动协同定位效果优于无源被动定位,典型场景下高程定位精度显著提升约96.33%。此外,所提的空能资源优化算法在求解空能资源联合优化问题时,性能优于标准(传统)灰狼算法、改进灰狼算法等。

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.