MULTI-TARGET LOCALIZATION IN UNDERWATER ACOUSTIC SENSOR NETWORKS BASED ON COMPRESSIVE SAMPLING MATCHING PURSUIT

被引：0
|
作者：
Wang, Biao [1 ]
Zhu, Zhihui [1 ]
Ge, Huilin [1 ]
Dai, Yuewei [1 ]
机构：
[1] Jiangsu Univ Sci & Technol, Sch Elect & Informat, 2 Mengxi Rd, Zhenjiang 212003, Peoples R China
来源：
INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2014年 / 10卷 / 06期
关键词：
Underwater sensor networks; Target localization; Compressive sensing;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A novel underwater multi-target localization (UML) method based on underwater acoustic sensor networks is proposed, which solves the target localization problem combining underwater acoustic transmission theory with compressed sensing (CS) theory. The proposed UML algorithm studies the sparsity of target localization in underwater space, and sets up the sparsity localization model with the received signal strength by sensors as dictionary, which is estimated by studying the underwater acoustic transmission theory. Finally, fewer numbers of underwater sensor nodes are randomly selected to recover the target location using Compressive Sampling Matching Pursuit (CoSaMP) algorithm based on CS. The UML algorithm achieves better localization performance, and meanwhile the time consuming of compute is decreased.
引用
收藏
页码：2167 / 2177
页数：11
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