Indoor location learning over wireless fingerprinting system with particle markov chain model

Sok-Ian Sou, Wen Hsiang Lin, Kun-Chan Lan, Chuan Sheng Lin

研究成果: Article

2 引文 (Scopus)

摘要

This paper describes research toward 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 the cellular network, this paper 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 LA-level performance regarding the visibility area in an environment with large obstacles. In the presence of sight obstructions, BTrack is evaluated using a real-world test bed built in a 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 the direct line-of-sight. Compared with the 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.

原文English
文章編號8601189
頁(從 - 到)8713-8725
頁數13
期刊IEEE Access
7
DOIs
出版狀態Published - 2019 一月 1

指紋

Bluetooth
Markov processes
Visibility
Statistical methods
Costs

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

引用此文

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abstract = "This paper describes research toward 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 the cellular network, this paper 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 LA-level performance regarding the visibility area in an environment with large obstacles. In the presence of sight obstructions, BTrack is evaluated using a real-world test bed built in a 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 the direct line-of-sight. Compared with the 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.",
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Indoor location learning over wireless fingerprinting system with particle markov chain model. / Sou, Sok-Ian; Lin, Wen Hsiang; Lan, Kun-Chan; Lin, Chuan Sheng.

於: IEEE Access, 卷 7, 8601189, 01.01.2019, p. 8713-8725.

研究成果: Article

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