基于TDOA的网络化定位系统自适应站点优选算法

网络化无源多点时差定位系统具有部署灵活、易于扩展及无电磁辐射等特点,特别适合航天发射场等大规模场所的部署,实现对低空机动目标的定位。但当网络站点数量较多时,对所有站点进行信号采集传输将造成大量能量及网络资源的浪费。针对不同站点数量和站点位置对定位精度产生的影响,结合当前各站点无人机信号识别结果,提出了一种自适应站点优选算法,该算法基于Cramer-Rao界均值最小化原则,利用K均值聚类算法动态调整当前目标定位空间,可在密集部署的传感器站点中快速选择出符合定位要求的站点集。仿真结果表明,提出的自适应站点优选算法可有效提高网络定位精度。

Adaptive Station Optimization Algorithm for Networked Passive Location System Based on TDOA

Networked passive location system has the characteristics of flexible deployment,easy expansion and no electromagnetic radiation,which makes it particularly suitable for the location of the flying targets moving in low altitude at space launch site.However,when the number of network stations is large,signal acquisition and transmission of all stations will cause a lot of energy and network resources waste.In view of the effects of both the number and the location of sites,this paper proposes an adaptive optimize embattle method.Based on minimization the average Cramer-Rao Lower Bounds of target position space rule,the algorithm enables the dynamic adjustment of current target location space by the use of UAV signal recognition and K-means clustering algorithm.Simulation results show that the proposed algorithm can achieve high positioning accuracy for TDOA passive positioning.