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J. Sens. Actuator Netw. 2018, 7(1), 7; doi:10.3390/jsan7010007

Enhanced Sparse Representation-Based Device-Free Localization with Radio Tomography Networks

1
Department of Electronics Engineering, Huizhou University, Huizhou 516007, China
2
School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
3
College of Information Science and Technology, Jinan University, Guangzhou 510632, China
*
Author to whom correspondence should be addressed.
Received: 8 December 2017 / Revised: 2 February 2018 / Accepted: 5 February 2018 / Published: 8 February 2018
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Abstract

The sparse distribution of targets in monitored areas is an important prior for device-free localization (DFL) with radio tomography networks. In this article, our goal is to develop an enhanced sparse representation-based DFL method that takes the full potential of sparsity for location reconstruction. An expanded sensing matrix spanning the concatenation of a sampling matrix and a unit error-correcting base is proposed for modelling the measurement process. The sampling matrix can either be composed of the ellipse model from calibrated networks or the received signal strength (RSS) fingerprint-based model induced by training samples with one person at predefined locations. Thus, the sparsity of targets is enhanced under the expanded sensing matrix and the 1 -minimization-based approximations are derived for the recovery of locations. Experimental studies in an open outdoor scenario, in a line-of-sight (LOS) indoor scenario, and in a non-line-of-sight (NLOS) indoor scenario, are conducted to verify the efficacy of the proposed method. View Full-Text
Keywords: enhanced sparse representation; expanded sensing matrix; device-free localization; radio tomography networks enhanced sparse representation; expanded sensing matrix; device-free localization; radio tomography networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Liu, T.; Luo, X.; Liang, Z. Enhanced Sparse Representation-Based Device-Free Localization with Radio Tomography Networks. J. Sens. Actuator Netw. 2018, 7, 7.

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