Robust indoor localization and tracking using GSM fingerprints - Sorbonne Université
Article Dans Une Revue EURASIP Journal on Wireless Communications and Networking Année : 2015

Robust indoor localization and tracking using GSM fingerprints

Résumé

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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hal-01215146 , version 1 (13-10-2015)

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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, 2015, pp.157. ⟨10.1186/s13638-015-0401-7⟩. ⟨hal-01215146⟩
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