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同行評審

17 引文 斯高帕斯(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

All Science Journal Classification (ASJC) codes

  • 一般電腦科學
  • 一般材料科學
  • 一般工程

指紋

深入研究「Indoor location learning over wireless fingerprinting system with particle markov chain model」主題。共同形成了獨特的指紋。

引用此