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Robust indoor localization and tracking using GSM fingerprints

Ye Tian 1, * Bruce Denby 1 Iness Ahriz 2 Pierre Roussel 3 Gérard Dreyfus 3
* Corresponding author
3 SIGMA - Laboratoire Signaux, Modèles et Apprentissage Statistique
ESPCI Paris - Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris, CNRS - Centre National de la Recherche Scientifique : UMR7084
Abstract : The article presents an easy to implement approach for indoor localization and navigation that combines Bayesian filtering with support vector machine classifiers to associate high-dimensionality cellular telephone network received signal strength fingerprints to distinct spatial regions. The technique employs a " space sampling " and a " time sampling " scheme in the training procedure, and the Bayesian filter allows introducing a priori information on room layout and target trajectories, resulting in robust room-level indoor localization.
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Submitted on : Tuesday, October 13, 2015 - 3:48:14 PM
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Ye Tian, Bruce Denby, Iness Ahriz, Pierre Roussel, Gérard Dreyfus. Robust indoor localization and tracking using GSM fingerprints. EURASIP Journal on Wireless Communications and Networking, SpringerOpen, 2015, pp.157. ⟨10.1186/s13638-015-0401-7⟩. ⟨hal-01215146⟩



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