Bluetooth positioning based on weighted K-nearest neighbors and adaptive bandwidth mean shift

Qi Wang, Rui Sun, Xiangde Zhang, Yanrui Sun and Xiaojun Lu

International Journal of Distributed Sensor Networks, 2017, vol. 13, issue 5, 1550147717706681

Abstract: Bluetooth positioning is an important and challenging topic in indoor positioning. Although a lot of algorithms have been proposed for this problem, it is still not solved perfectly because of the instable signal strengths of Bluetooth. To improve the performance of Bluetooth positioning, this article proposes a coarse-to-fine positioning method based on weighted K-nearest neighbors and adaptive bandwidth mean shift. The method first employs weighted K-nearest neighbors to generate multi-candidate locations. Then, the testing position is obtained by applying adaptive bandwidth mean shift to the multi-candidate locations, which is used to search for the maximum density of the candidate locations. Experimental result indicates that the proposed method improves the performance of Bluetooth positioning.

Keywords: Indoor positioning; Bluetooth; weighted K-nearest neighbors; adaptive bandwidth mean shift (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:13:y:2017:i:5:p:1550147717706681

DOI: 10.1177/1550147717706681

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