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

• Main Authors: Tong Liu, Xiaomu Luo, Zhuoqian Liang
• Format: Article
• Language: English
• Published: MDPI AG 2018-02-01
• Series: Journal of Sensor and Actuator Networks
• Subjects: enhanced sparse representation; expanded sensing matrix; device-free localization; radio tomography networks
• Online Access: http://www.mdpi.com/2224-2708/7/1/7

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 ℓ 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.