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.

How to Cite this Article

Iness Ahriz, Yacine Oussar, Bruce Denby, and Gérard Dreyfus, “Full-Band GSM Fingerprints for Indoor Localization Using a Machine Learning Approach,” International Journal of Navigation and Observation, vol. 2010, Article ID 497829, 7 pages, 2010. https://doi.org/10.1155/2010/497829.