一种基于子空间方法的近场目标定位算法  

Near-Field Target Localization Algorithm Based on Subspace Method

作　　者：阮新志 吴云韬[1,2] 黄龙庭 RUAN Xinzhi;WU Yuntao;HUANG Longting(Hubei Key Laboratory of Intelligent Robot(Wuhan Institute of Technology),Wuhan 430205,China;School of Computer Science&Engineering,Wuhan Institute of Technology,Wuhan 430205,China;School of Information Engineering,Wuhan University of Technology,Wuhan 430070,China)

机构地区：[1]智能机器人湖北重点实验室(武汉工程大学),湖北武汉430205 [2]武汉工程大学计算机科学与工程学院,湖北武汉430205 [3]武汉理工大学信息工程学院,湖北武汉430070

出　　处：《武汉工程大学学报》2021年第3期313-317,共5页Journal of Wuhan Institute of Technology

基　　金：国家自然科学基金(61771353)。

摘　　要：针对近场目标参数估计中计算量大的问题,提出了一种结合主奇异向量模态分析(PUMA)技术以及一维多重信号分类(MUSIC)方法的近场目标定位算法。该算法首先利用PUMA技术估计近场目标角度参数,然后通过估计出的角度参数并结合一维MUSIC方法,估计出近场目标距离参数。通过计算机仿真实验表明,该方法能明显减少近场目标参数估计的计算量,并且具有较好的参数估计性能。To reduce the computational overhead of near-field target parameter estimation,a near-field target localization algorithm based on the principal singular vector utilization for modal analysis(PUMA)and onedimensional multiple signal classification method(MUSIC)was proposed.Firstly,the PUAM technique was used to estimate the angle parameters of near-field targets.Then,the one-dimensional MUSIC spectrum was constructed from the estimated angle parameters to estimate the range parameters of near-field targets.Simulation results show that the proposed method can significantly reduce the computational overhead of the near-field target parameter estimation.Above all,better performance can be achieved.

关 键 词：近场目标 主奇异向量模态分析 MUSIC 角度估计 距离估计 定位算法 

分 类 号：TN911.72[电子电信—通信与信息系统]