RESEARCH ARTICLE
Practical Classification Methods for Indoor Positioning
Mahsa Honary*, Lyudmila Mihaylova, Costas Xydeas
Article Information
Identifiers and Pagination:
Year: 2012Volume: 6
First Page: 31
Last Page: 38
Publisher ID: TOTJ-6-31
DOI: 10.2174/1874447801206010031
Article History:
Received Date: 27/5/2012Revision Received Date: 20/6/2012
Acceptance Date: 24/6/2012
Electronic publication date: 25/5/2012
Collection year: 2012
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.
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.