Table of Contents
International Journal of Navigation and Observation
Volume 2010, Article ID 497829, 7 pages
http://dx.doi.org/10.1155/2010/497829
Research Article

Full-Band GSM Fingerprints for Indoor Localization Using a Machine Learning Approach

1Signal Processing and Machine Learning (SIGMA) Laboratory, ESPCI—ParisTech, 10 rue Vauquelin, 75005 Paris, France
2Université Pierre et Marie Curie—Paris VI, 4 place Jussieu, 75005 Paris, France

Received 1 October 2009; Accepted 25 March 2010

Academic Editor: Simon Plass

Copyright © 2010 Iness Ahriz et al. 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.

Abstract

Indoor handset localization in an urban apartment setting is studied using GSM trace mobile measurements. Nearest-neighbor, Support Vector Machine, Multilayer Perceptron, and Gaussian Process classifiers are compared. The linear Support Vector Machine provides mean room classification accuracy of almost 98% when all GSM carriers are used. To our knowledge, ours is the first study to use fingerprints containing all GSM carriers, as well as the first to suggest that GSM can be useful for localization of very high performance.