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PBIL: PDR for scalable Bluetooth Indoor Localization


Subramanian,  SP
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Subramanian, S., Sommer J, Zeh F-P, Schmitt, S., & Rosenstiel, W. (2009). PBIL: PDR for scalable Bluetooth Indoor Localization. Proceedings of the Third International Conference on Next Generation Mobile Applications, Services and Technologies (NGMAST 2009), 170-175.

The ever-existing problems in indoor localization are attaining efficient scalability, reliability and accuracy besides cost efficiency. Although diverse approaches based on WLAN, Bluetooth, ZigBee, RFID and UWB had been divulged for localization and navigation, these schemes often do lack competent localization. Furthermore these schemes linger either expensive or be deficient in accuracy. Moreover not all technologies exist in smart phones. Recent outburst of technology expansion has paved way to solve indoor localization complexity by cohering various positioning methods and sensor models. In this paper, we developed a unified particle filter based localization approach based on Bluetooth positioning and pedestrian dead reckoning (PDR) method. We call this system as PBIL (PDR for scalable Bluetooth Indoor Localization). The proposed approach overcomes failure in the context of uninterrupted service where Bluetooth technologies lag behind. By coalescing Bluetooth and 3D compass sensor models, we surmount th e localizing tribulations and guarantee a preeminent solution for pedestrian indoor localization and navigation.