Indoor Location Learning over Wireless Fingerprinting System with Particle Markov Chain Model

Abstract

This paper describes research towards a tracking system for locating persons indoor based on low-cost Bluetooth Low Energy (BLE) beacons.Wireless fingerprinting based on BLE beacons has emerged as an increasingly popular solution for fine-grained indoor localization. Inspired by the idea of mobility tracking used in cellular network, the present study proposes a BLE-based tracking system, designated as BTrack, to learn the location area (LA) of an indoor user based on the reported wireless fingerprinting combined with statistical analysis. We propose a new particle Markov Chain model to evaluate the LAlevel performance regarding the visibility area with large obstacles environment. In the presence of sight obstructions, BTrack is evaluated using an indoor testbed built in a real-world library with tall bookshelves. The performance of the proposed system is evaluated in terms of the mean distance error and the LA prediction accuracy considering direct line-of-sight. It is shown that compared with existing methods, BTrack reduces the average localization error by 25% and improves the average prediction accuracy by more than 16% given a random mobility pattern through the testbed area.

Citation

Sok-Ian Sou, Wen-Hsiang Lin, Kun-Chan Lan, and Chuan-Sheng Lin"Indoor Location Learning over Wireless Fingerprinting System with Particle Markov Chain Model"

Bitex

@ARTICLE{lan2018: ,
AUTHOR = {Sok-Ian Sou, Wen-Hsiang Lin, Kun-Chan Lan, and Chuan-Sheng Lin},
TITLE = {Indoor Location Learning over Wireless Fingerprinting System with Particle Markov Chain Model},
BOOKTITLE = {IEEE Acess},
YEAR = {2018}
}

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