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.

Citations to this Article [8 citations]

The following is the list of published articles that have cited the current article.

  • Marzieh Dashti, and Holger Claussen, “Extracting Location Information from RF Fingerprints,” 2016 IEEE Globecom Workshops (GC Wkshps), pp. 1–6, . View at Publisher · View at Google Scholar
  • Ye Tian, Bruce Denby, Iness Ahriz, Pierre Roussel, and Gerard Dreyfus, “Fast, handset-based GSM fingerprints for indoor localization,” 2012 International Symposium on Wireless Communication Systems (ISWCS), pp. 641–645, . View at Publisher · View at Google Scholar
  • Chihhsiong Shih, and Chaolong Liang, “The improvement of indoor localization precision through partial least square(PLS) and swarm(PSO) methods,” 2018 IEEE Sensors Applications Symposium (SAS), pp. 1–6, . View at Publisher · View at Google Scholar
  • Raida Zouari, Iness Ahriz, Rafik Zayani, Ali Dziri, and Ridha Bouallegue, “Relevant CIR parameters selection for fingerprinting based location algorithm,” 2015 23rd International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 170–173, . View at Publisher · View at Google Scholar
  • Marzieh Dashti, and Holger Claussen, “A metric to describe access point significance in location estimation,” 2016 13th Workshop on Positioning, Navigation and Communications (WPNC), pp. 1–6, . View at Publisher · View at Google Scholar
  • Yacine Oussar, Iness Ahriz, Bruce Denby, and Gérard Dreyfus, “Indoor localization based on cellular telephony RSSI fingerprints containing very large numbers of carriers,” EURASIP Journal on Wireless Communications and Networking, vol. 2011, no. 1, 2011. View at Publisher · View at Google Scholar
  • Majda Petric, Aleksandar Neskovic, Natasa Neskovic, and Milos Borenovic, “Indoor Localization Using Multi-operator Public Land Mobile Networks and Support Vector Machine Learning Algorithms,” Wireless Personal Communications, 2018. View at Publisher · View at Google Scholar
  • Priya Roy, Chandreyee Chowdhury, Dip Ghosh, and Sanghamitra Bandyopadhyay, “JUIndoorLoc: A Ubiquitous Framework for Smartphone-Based Indoor Localization Subject to Context and Device Heterogeneity,” Wireless Personal Communications, 2019. View at Publisher · View at Google Scholar