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Sensors 2016, 16(11), 1845; doi:10.3390/s16111845

RSS Fingerprint Based Indoor Localization Using Sparse Representation with Spatio-Temporal Constraint

1
Beijing Advanced Innovation Center for Future Internet Technology, Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing University of Technology, Beijing 100124, China
2
Beijing Transportation Information Center, Beijing 100073, China
3
Beijing Transportation Coordination Center, Beijing 100073, China
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard Reindl
Received: 3 August 2016 / Revised: 28 September 2016 / Accepted: 17 October 2016 / Published: 3 November 2016
(This article belongs to the Section Sensor Networks)
View Full-Text   |   Download PDF [819 KB, uploaded 3 November 2016]   |  

Abstract

The Received Signal Strength (RSS) fingerprint-based indoor localization is an important research topic in wireless network communications. Most current RSS fingerprint-based indoor localization methods do not explore and utilize the spatial or temporal correlation existing in fingerprint data and measurement data, which is helpful for improving localization accuracy. In this paper, we propose an RSS fingerprint-based indoor localization method by integrating the spatio-temporal constraints into the sparse representation model. The proposed model utilizes the inherent spatial correlation of fingerprint data in the fingerprint matching and uses the temporal continuity of the RSS measurement data in the localization phase. Experiments on the simulated data and the localization tests in the real scenes show that the proposed method improves the localization accuracy and stability effectively compared with state-of-the-art indoor localization methods. View Full-Text
Keywords: indoor localization; RSS fingerprint; sparse representation; temporal constraint; spatial constraint indoor localization; RSS fingerprint; sparse representation; temporal constraint; spatial constraint
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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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MDPI and ACS Style

Piao, X.; Zhang, Y.; Li, T.; Hu, Y.; Liu, H.; Zhang, K.; Ge, Y. RSS Fingerprint Based Indoor Localization Using Sparse Representation with Spatio-Temporal Constraint. Sensors 2016, 16, 1845.

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