EFFICIENT SEMIDEFINITE RELAXATION FOR ENERGY-BASED SOURCE LOCALIZATION IN SENSOR NETWORKS

被引：18
|
作者：
Wang, Gang [1 ]
Yang, Kehu [1 ]
机构：
[1] Xidian Univ, ISN Lab, Xian 710071, Peoples R China
来源：
2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1- 8, PROCEEDINGS | 2009年
关键词：
Acoustic energy; Source localization; Maximum-likelihood; Sensor networks; Semidefinite relaxation (SDR); OPTIMIZATION;
D O I：
10.1109/ICASSP.2009.4960069
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
Recently, energy-based localization using acoustic energy measurements has received much attention in wireless sensor networks. Since the objective function of the energy-based maximum likelihood (ML) localization is non-convex, the global solutions are hardly obtained without good initial estimates. In this paper, we relax this non-convex problem as a convex semidefinite programming (SDP), based on which a good estimate can be obtained and be improved by a procedure called randomization. Simulation results show that the proposed method is effective and outperforms the existing methods.
引用
收藏
页码：2257 / 2260
页数：4
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