Compressive sensing based positioning using RSS of WLAN access points

Authors:
Chen Feng;Wain Sy Anthea Au;Shahrokh Valaee;Zhenhui Tan
Affiliations:
Department of Electrical and Computer Engineering, University of Toronto and State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University;Department of Electrical and Computer Engineering, University of Toronto;Department of Electrical and Computer Engineering, University of Toronto;State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University
Venue:
INFOCOM'10 Proceedings of the 29th conference on Information communications
Year:
2010

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Abstract

The sparse nature of location finding problem makes the theory of compressive sensing desirable for indoor positioning in Wireless Local Area Networks (WLANs). In this paper, we address the received signal strength (RSS)-based localization problem in WLANs using the theory of compressive sensing (CS), which offers accurate recovery of sparse signals from a small number of measurements by solving an l1-minimization problem. A pre-processing procedure of orthogonalization is used to induce incoherence needed in the CS theory. In order to mitigate the effects of RSS variations due to channel impediments, the proposed positioning system consists of two steps: coarse localization by exploiting affinity propagation, and fine localization by the CS theory. In the fine localization stage, access point selection problem is studied to further increase the accuracy. We implement the positioning system on a WiFi-integrated mobile device (HP iPAQ hx4700 with Windows Mobile 2003 Pocket PC) to evaluate the performance. Experimental results indicate that the proposed system leads to substantial improvements on localization accuracy and complexity over the widely used traditional fingerprinting methods.