Hierarchical Greedy Matching Pursuit for Multi-target Localization in Wireless Sensor Networks Using Compressive Sensing

被引：0
|
作者：
You K.-Y. [1 ,2 ]
Yang L.-S. [1 ]
Guo W.-B. [1 ,2 ]
机构：
[1] Beijing University of Posts and Telecommunications, Beijing
[2] Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory, Shijiazhuang
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2019年 / 45卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Compressive sensing (CS); Greedy algorithm; Hierarchical algorithm; Multi-target localization; Wireless sensor network (WSN);
D O I：
10.16383/j.aas.2018.c170237
中图分类号：
学科分类号：
摘要：
This paper addresses the problem of multi-target localization in wireless sensor networks using compressive sensing (CS). We first analyze the ample implicit structured information contained in the multi-target localization scenario. Then, a hierarchical greedy matching pursuit (HGMP) approach for multi-target localization is proposed. In the proposed HGMP algorithm, the possible positions of targets in the meshing space are obtained as the global estimation layer, and subsequently the global estimation information is used as {{the input} information to the sparse recovery layer to reconstruct the multi-target localization vector in the meshing space. Moreover, we prove that the orthogonality-based preprocessing operation widely adopted in the literature reduces the signal-to-noise ratio (SNR), degrades the localization performance, a problem that has never been addressed before. Through the global estimation layer, impossible positions are preliminarily removed, which is equivalent to separating the signal subspace from the observation subspace, thus reducing the influence of the observed noise. Finally, theoretical analysis and computer simulations show that the proposed algorithm enjoys a linear computational complexity and a higher localization accuracy at the same SNR. Copyright © 2019 Acta Automatica Sinica. All rights reserved.
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页码：480 / 489
页数：9
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