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 (BT) architecture.
Bluetooth indoor localization with multiple neural networks / Altini M. ; Brunelli D. ; Farella E. ; Benini L.. - STAMPA. - (2010), pp. 295-300. (Intervento presentato al convegno Wireless Pervasive Computing (ISWPC), 2010 5th IEEE International Symposium on tenutosi a Modena, Italy nel 5-7 May 2010).
Bluetooth indoor localization with multiple neural networks
BRUNELLI, DAVIDE;FARELLA, ELISABETTA;BENINI, LUCA
2010
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 (BT) architecture.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
