Sparse Target Localization in RF Sensor Networks using Compressed Sensing
作者单位：School of Computer Science and Telecommunication Engineering Jiangsu University School of Information Science and Technology Sun Yat-Sen University
会议名称：《第25届中国控制与决策会议》
会议届次：25th
主办单位：IEEE;NE Univ;IEEE Ind Elect Chapter;IEEE Harbin Sect Control Syst Soc Chapter;Guizhou Univ;IEEE Control Syst Soc;Syst Engn Soc China;Chinese Assoc Artificial Intelligence;Chinese Assoc Automat;Tech Comm Control Theory;Chinese Assoc Aeronaut;Automat Control Soc;Chinese Assoc Syst Simulat;Simulat Methods & Modeling Soc;Intelligent Control & Management Soc
会议日期：2013年
学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 080202[工学-机械电子工程] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 0802[工学-机械工程]
基 金：supported by the National Science Foundation of China under Grant no. 61074167, 61170126, 61202110 the Scientific Research Foundation for Advanced Talents by the Jiangsu University, no. 12JDG050
关 键 词：Target Localization Sparse Recovery Compressed Sensing RF Sensor Networks
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.