Bayesian compressive sensing algorithm for multiple target localization

被引：0

作者：

Wu, Zhefu ^{[1
]}

Xu, Limin ^{[1
]}

Chen, Bin ^{[2
]}

Qin, Yali ^{[1
]}

机构：

[1] School of Information, Zhejiang University of Technology, Hangzhou

[2] School of Art, Zhejiang University of Technology, Hangzhou

来源：

Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2014年 / 35卷 / 10期

关键词：

Bayesian compressive sensing; Multiple target localization; RSS; Sensor networks;

D O I：

10.3969/j.issn.1006-7043.201306064

中图分类号：

学科分类号：

摘要：

In order to reduce the overhead of the network system while maintaining the sufficient accuracy of indoor localization, Bayesian compressed sensing and Laplace prior model were explored to solve indoor localization and data compressing of multiple wireless devices. The proposed indoor positioning system was based on received signal strength (RSS) measurement. It was followed by compressing the RSS with random projection on the multiple wireless devices and making accumulation after transmitting them to the center server. The locations of these targets were determined by collecting RSS based on the algorithm of Bayesian compressive sensing using Laplace priors, by combining the maximum likelihood procedure and iterative approximation algorithm. Simulation results showed that the multiple targets localization using Bayesian compressive sensing had at least 52.2% more accuracy compared to the orthogonal matching pursuit (OMP) algorithm and had at least 13.7% more accuracy compared to the basis pursuit (BP) algorithm.

引用

页码：1282 / 1287

页数：5

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