An accurate indoor positioning algorithm using particle filter based on the proximity of bluetooth beacons

Ryoya Momose; Tomoyuki Nitta; Masao Yanagisawa; Nozomu Togawa

doi:10.1109/GCCE.2017.8229229

An accurate indoor positioning algorithm using particle filter based on the proximity of bluetooth beacons

Ryoya Momose, Tomoyuki Nitta, Masao Yanagisawa, Nozomu Togawa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

Abstract

Indoor positioning without GPS is one of the most important problems in indoor pedestrian navigation. In this paper, we propose an accurate indoor positioning algorithm using a particle filter based on a floormap, where we use the proximity of the Bluetooth beacons as well as acceleration and geomagnetic sensors. In designing the likelihood function in the particle filter, we effectively use the proximity of the Bluetooth beacons, which just gives rough distance to the target beacon but more stable than conventional RSSI-based distance estimation. In addition to that, by effectively utilizing a floormap, the accumulated positioning errors due to the acceleration and geomagnetic sensors are much reduced. Moreover, when the radio waves from the Bluetooth beacons are blocked by obstacles, we can also take it into account in designing the likelihood function in the particle filter. Experimental results demonstrate that our algorithm can reduce the indoor positioning errors by up to 79% compared to several conventional algorithms.

Original language	English
Title of host publication	2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-5
Number of pages	5
Volume	2017-January
ISBN (Electronic)	9781509040452
DOIs	https://doi.org/10.1109/GCCE.2017.8229229
Publication status	Published - 2017 Dec 19
Event	6th IEEE Global Conference on Consumer Electronics, GCCE 2017 - Nagoya, Japan Duration: 2017 Oct 24 → 2017 Oct 27

Other

Other	6th IEEE Global Conference on Consumer Electronics, GCCE 2017
Country	Japan
City	Nagoya
Period	17/10/24 → 17/10/27

ASJC Scopus subject areas

Media Technology
Instrumentation
Electrical and Electronic Engineering

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An accurate indoor positioning algorithm using particle filter based on the proximity of bluetooth beacons. / Momose, Ryoya; Nitta, Tomoyuki; Yanagisawa, Masao ; Togawa, Nozomu.

2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-5.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Momose, R, Nitta, T, Yanagisawa, M & Togawa, N 2017, An accurate indoor positioning algorithm using particle filter based on the proximity of bluetooth beacons. in 2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-5, 6th IEEE Global Conference on Consumer Electronics, GCCE 2017, Nagoya, Japan, 17/10/24. https://doi.org/10.1109/GCCE.2017.8229229

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abstract = "Indoor positioning without GPS is one of the most important problems in indoor pedestrian navigation. In this paper, we propose an accurate indoor positioning algorithm using a particle filter based on a floormap, where we use the proximity of the Bluetooth beacons as well as acceleration and geomagnetic sensors. In designing the likelihood function in the particle filter, we effectively use the proximity of the Bluetooth beacons, which just gives rough distance to the target beacon but more stable than conventional RSSI-based distance estimation. In addition to that, by effectively utilizing a floormap, the accumulated positioning errors due to the acceleration and geomagnetic sensors are much reduced. Moreover, when the radio waves from the Bluetooth beacons are blocked by obstacles, we can also take it into account in designing the likelihood function in the particle filter. Experimental results demonstrate that our algorithm can reduce the indoor positioning errors by up to 79% compared to several conventional algorithms.",

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An accurate indoor positioning algorithm using particle filter based on the proximity of bluetooth beacons

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