Fast, handset-based GSM fingerprints for indoor localization

Ye Tian (1) , Bruce Denby (1) , Iness Ahriz Roula (2, 3) , Pierre Roussel (1) , Gérard Dreyfus (1)

1 SIGMA - Laboratoire Signaux, Modèles et Apprentissage Statistique
2 CEDRIC - Centre d'études et de recherche en informatique et communications
3 CEDRIC - LAETITIA - CEDRIC. Traitement du signal et architectures électroniques

Accurately localizing users in indoor environments remains an important and challenging task. The article presents new results on room-level indoor localization, using cellular Received Signal Strength fingerprints collected with a standard cellular handset programmed to perform fast scans of the 900 and 1800 Megahertz GSM bands as a user explores an indoor environment at a normal walking pace. Support Vector Machines are used to deal with the high dimensionality of the fingerprints. The study demonstrates that an appropriately programmed standard cellular handset can provide a simple, inexpensive solution for accurate room-level indoor localization.

Mots clés: localization; indoor; fingerprint; machine learning; support vector machine

Domaines: Informatique ubiquitaire

Ye Tian, Bruce Denby, Iness Ahriz Roula, Pierre Roussel, Gérard Dreyfus. Fast, handset-based GSM fingerprints for indoor localization. 2012 International Symposium on Wireless Communication Systems (ISWCS), Aug 2012, France. pp.641-645, ⟨10.1109/ISWCS.2012.6328446⟩. ⟨hal-00961277⟩