基于稀疏信号重构的无线传感网络目标定位  

Target localization in wireless sensor networks using sparse signal reconstruction

作　　者：王勇[1] 王雪[1] 孙欣尧[1] 

机构地区：[1]清华大学精密仪器与机械学系精密测试技术及仪器国家重点实验室,北京100084

出　　处：《仪器仪表学报》2012年第2期362-368,共7页Chinese Journal of Scientific Instrument

基　　金：国家重点基础研究发展计划(973计划)(2006CB303000);国家自然科学基金(60970103;60673176;60373014;50175056);国家教育部博士点基金(20090002110016)资助项目

摘　　要：提出了一种新的基于稀疏信号重构的无线传感网络目标定位方法。针对目标定位问题,将多目标位置表示为离散化测量空间上的稀疏向量,则多传感节点声音信号能量测量值向量可分解为测量矩阵、稀疏矩阵与稀疏向量的乘积,通过稀疏信号重构方法可以恢复目标位置稀疏向量,实现多目标定位。传统L1范数稀疏信号重构法要求测量矩阵和稀疏矩阵乘积满足受限等距性条件,在目标定位问题中难以满足。采用贪婪匹配追踪算法重构稀疏向量,基于噪声信号能量幅值终止迭代搜索,进行多目标定位。实验表明,基于贪婪匹配追踪稀疏信号重构目标定位方法能准确实现多目标定位,定位精度优于基于正交匹配追踪的稀疏信号重构目标定位方法和基于单纯形搜索的最大似然估计目标定位方法。This paper proposes a new target localization method based on sparse signal reconstruction. The positions of multiple targets are denoted by a sparse vector in discrete space. The signal energy measurement values of wireless sensors are decomposed to the product of measurement matrix, sparse matrix and sparse vector. The sparse vector can be recovered with sparse signal reconstruction, thus the localization of the targets can be realized. Traditional L1 norm reconstruction method requires that the product of the measurement matrix and sparse matrix satisfies the re- stricted isometry property, which is difficult to be realized in target localization. This paper adopts orthogonal matc- hing pursuit and greedy matching pursuit to reconstruct the sparse vector. Experiments verify that our proposed target localization method based on sparse signal reconstruction can localize multiple targets precisely, and has a better per- formance than the sparse signal reconstruction target localization method based on orthogonal matching pursuit and maximum likelihood estimation method based on Nelder-Mead simplex method.

关 键 词：稀疏信号重构 目标定位 压缩测量 无线传感网络 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]