RESEARCH ARTICLE


Practical Classification Methods for Indoor Positioning



Mahsa Honary*, Lyudmila Mihaylova, Costas Xydeas
Lancaster University, South Drive, Lancaster LA1 4WA, UK.


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Creative Commons License
© 2012 Honary;

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Lancaster University, South Drive, Lancaster LA1 4WA, UK; Tel: +441524510388; Fax: +44 1524 510493; E-mail: m.honary@lancaster.ac.uk


Abstract

Location awareness is of primary importance in a wealth of applications such as transportation, mobile health systems, augmented reality and navigation. For example, in busy transportation areas (such as airports) providing clear, personalised notifications and directions, can reduce delays and improve the passenger journeys. Currently some applications provide easy access to information. These travel related applications can become context aware via the availability of accurate indoor/outdoor positioning. However, there are barriers that still have to overcome. One such barrier is the time required to set up and calibrate indoor positioning systems, another is the challenge of scalability with regard to the processing requirements of indoor positioning algorithms. This paper investigates the relationship between the calibration data and positioning system accuracy and analyses the performance of a k-Nearest Neighbour (k-NN) based positioning algorithm using real GSM data. Furthermore, the paper proposes a positioning scheme based on Gaussian Mixture Models (GMM). Experimental results show that the proposed GMM algorithm (without post-filtering) provides high levels of localization accuracy and successfully copes with the scalability problems that the conventional k- NN approach faces.

Keywords: Indoor positioning, Received Signal Strengths, asset tracking, informed traveller.