Title: iPil: improving passive indoor localisation via link-based CSI features

Authors: Liangyi Gong; Wu Yang; Dapeng Man; Jiguang Lv

Addresses: School of Computer and Communication Engineering, Tianjin University of Technology, Tianjin 300384, China ' Department of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China ' Department of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China ' Department of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China

Abstract: Passive indoor localisation acts as a key enabler for various emerging applications such as secured region monitoring, smart homes, intelligent nursing, etc. Despite of years of research, their accuracy of localisation still remains unsatisfactory for practical uses. The main hurdle lies in the coarse measurement of wireless channels, e.g., received signal strength indicator (RSSI), employed in most existing schemes. In this work, we explore the potential of using channel state information (CSI) for fine-grained passive indoor localisation on a single communication link. To achieve high accuracy, we propose a solution based CSI fingerprint and devise two novel localisation estimator approaches suited to different conditions: weighted Bayesian (WBayes) and the maximum similarity metric (MSM). Compared with RSSI, CSI has demonstrated itself with a high accuracy of location distinction. Experimental results show that our schemes can achieve a higher accuracy.

Keywords: passive indoor localisation; CSI fingerprint; channel state information; physical layer; localisation estimation; fine-grained localisation; weighted Bayes; maximum similarity.

DOI: 10.1504/IJAHUC.2016.078475

International Journal of Ad Hoc and Ubiquitous Computing, 2016 Vol.23 No.1/2, pp.36 - 45

Received: 15 Aug 2014
Accepted: 15 Dec 2014
Published online: 22 Aug 2016 *

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