Sparse Target Localization in RF Sensor Networks using Compressed Sensing

被引：0
|
作者：
Song, Heping [1 ]
Wang, Guoli [2 ]
Zhan, Yongzhao [1 ]
机构：
[1] Jiangsu Univ, Sch Comp Sci & Telecommun Engn, Zhenjiang 212013, Jiangsu, Peoples R China
[2] Sun Yat Sen Univ, Sch Informat Sci & Technol, Guangzhou 510006, Guangdong, Peoples R China
来源：
2013 25TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2013年
基金：
美国国家科学基金会;
关键词：
Target Localization; Sparse Recovery; Compressed Sensing; RF Sensor Networks;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we propose a greedy sparse recovery algorithm for target localization with RF sensor networks. The target spatial domain is discretized by grid pixels. When the network area consists only of several targets, the target localization is a sparsity-seeking problem such that the Compressed Sensing (CS) framework can be applied. We cast the target localization as a CS problem and solve it by the proposed sparse recovery algorithm, named the Residual Minimization Pursuit (RMP). The experimental studies are presented to demonstrate that the RMP offers an attractive alternative to OMP for sparse signal recovery, in addition, it is more favorable than non-CS based methods for target localization.
引用
收藏
页码：3999 / 4004
页数：6
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