Bluetooth indoor localization with multiple neural networks

Authors:
Marco Altini;Davide Brunelli;Elisabetta Farella;Luca Benini
Affiliations:
University of Bologna, Department of Electronics, Computer Sciences and Systems;University of Trento, Department of Information Engineering;University of Bologna, Department of Electronics, Computer Sciences and Systems;University of Bologna, Department of Electronics, Computer Sciences and Systems
Venue:
ISWPC'10 Proceedings of the 5th IEEE international conference on Wireless pervasive computing
Year:
2010

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Abstract

Over the last years, many different methods have been proposed for indoor localization and navigation services based on Radio frequency (RF) technology and Radio Signal Strength Indicator (RSSI). The accuracy achieved with such systems is typically low, mainly due to the variability of RSSI values, unsuitable for classic localization methods (e.g. triangulation). In this paper, we propose a novel approach based on multiple neural networks. We demonstrate with experimental results that by training and then activating different neural networks, tailored on the user orientation, high definition accuracy is achievable, allowing indoor navigation with a cost effective Bluetooth (DT) architecture.