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Sensors 2016, 16(6), 793; doi:10.3390/s16060793

Robust Statistical Approaches for RSS-Based Floor Detection in Indoor Localization

Department of Electronics and Communications Engineering, Tampere University of Technology, Tampere 33720, Finland
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Author to whom correspondence should be addressed.
Academic Editor: Gert F. Trommer
Received: 10 February 2016 / Revised: 17 May 2016 / Accepted: 25 May 2016 / Published: 31 May 2016
(This article belongs to the Section Physical Sensors)
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Abstract

Floor detection for indoor 3D localization of mobile devices is currently an important challenge in the wireless world. Many approaches currently exist, but usually the robustness of such approaches is not addressed or investigated. The goal of this paper is to show how to robustify the floor estimation when probabilistic approaches with a low number of parameters are employed. Indeed, such an approach would allow a building-independent estimation and a lower computing power at the mobile side. Four robustified algorithms are to be presented: a robust weighted centroid localization method, a robust linear trilateration method, a robust nonlinear trilateration method, and a robust deconvolution method. The proposed approaches use the received signal strengths (RSS) measured by the Mobile Station (MS) from various heard WiFi access points (APs) and provide an estimate of the vertical position of the MS, which can be used for floor detection. We will show that robustification can indeed increase the performance of the RSS-based floor detection algorithms. View Full-Text
Keywords: indoor localization; floor detection; RSS-based localization; robust regression; weighted centroid localization; trilateration indoor localization; floor detection; RSS-based localization; robust regression; weighted centroid localization; trilateration
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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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Razavi, A.; Valkama, M.; Lohan, E.S. Robust Statistical Approaches for RSS-Based Floor Detection in Indoor Localization. Sensors 2016, 16, 793.

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