Cargando…

An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window

The weighted K-nearest neighbor algorithm (WKNN) is easily implemented, and it has been widely applied. In the large-scale positioning regions, using all fingerprint data in matching calculations would lead to high computation expenses, which is not conducive to real-time positioning. Due to signal...

Descripción completa

Detalles Bibliográficos
Autores principales: Ruan, Ling, Zhang, Ling, Zhou, Tong, Long, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7766140/
https://www.ncbi.nlm.nih.gov/pubmed/33352918
http://dx.doi.org/10.3390/s20247269
_version_ 1783628647871021056
author Ruan, Ling
Zhang, Ling
Zhou, Tong
Long, Yi
author_facet Ruan, Ling
Zhang, Ling
Zhou, Tong
Long, Yi
author_sort Ruan, Ling
collection PubMed
description The weighted K-nearest neighbor algorithm (WKNN) is easily implemented, and it has been widely applied. In the large-scale positioning regions, using all fingerprint data in matching calculations would lead to high computation expenses, which is not conducive to real-time positioning. Due to signal instability, irrelevant fingerprints reduce the positioning accuracy when performing the matching calculation process. Therefore, selecting the appropriate fingerprint data from the database more quickly and accurately is an urgent problem for improving WKNN. This paper proposes an improved Bluetooth indoor positioning method using a dynamic fingerprint window (DFW-WKNN). The dynamic fingerprint window is a space range for local fingerprint data searching instead of universal searching, and it can be dynamically adjusted according to the indoor pedestrian movement and always covers the maximum possible range of the next positioning. This method was tested and evaluated in two typical scenarios, comparing two existing algorithms, the traditional WKNN and the improved WKNN based on local clustering (LC-WKNN). The experimental results show that the proposed DFW-WKNN algorithm enormously improved both the positioning accuracy and positioning efficiency, significantly, when the fingerprint data increased.
format Online
Article
Text
id pubmed-7766140
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-77661402020-12-28 An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window Ruan, Ling Zhang, Ling Zhou, Tong Long, Yi Sensors (Basel) Article The weighted K-nearest neighbor algorithm (WKNN) is easily implemented, and it has been widely applied. In the large-scale positioning regions, using all fingerprint data in matching calculations would lead to high computation expenses, which is not conducive to real-time positioning. Due to signal instability, irrelevant fingerprints reduce the positioning accuracy when performing the matching calculation process. Therefore, selecting the appropriate fingerprint data from the database more quickly and accurately is an urgent problem for improving WKNN. This paper proposes an improved Bluetooth indoor positioning method using a dynamic fingerprint window (DFW-WKNN). The dynamic fingerprint window is a space range for local fingerprint data searching instead of universal searching, and it can be dynamically adjusted according to the indoor pedestrian movement and always covers the maximum possible range of the next positioning. This method was tested and evaluated in two typical scenarios, comparing two existing algorithms, the traditional WKNN and the improved WKNN based on local clustering (LC-WKNN). The experimental results show that the proposed DFW-WKNN algorithm enormously improved both the positioning accuracy and positioning efficiency, significantly, when the fingerprint data increased. MDPI 2020-12-18 /pmc/articles/PMC7766140/ /pubmed/33352918 http://dx.doi.org/10.3390/s20247269 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ruan, Ling
Zhang, Ling
Zhou, Tong
Long, Yi
An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window
title An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window
title_full An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window
title_fullStr An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window
title_full_unstemmed An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window
title_short An Improved Bluetooth Indoor Positioning Method Using Dynamic Fingerprint Window
title_sort improved bluetooth indoor positioning method using dynamic fingerprint window
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7766140/
https://www.ncbi.nlm.nih.gov/pubmed/33352918
http://dx.doi.org/10.3390/s20247269
work_keys_str_mv AT ruanling animprovedbluetoothindoorpositioningmethodusingdynamicfingerprintwindow
AT zhangling animprovedbluetoothindoorpositioningmethodusingdynamicfingerprintwindow
AT zhoutong animprovedbluetoothindoorpositioningmethodusingdynamicfingerprintwindow
AT longyi animprovedbluetoothindoorpositioningmethodusingdynamicfingerprintwindow
AT ruanling improvedbluetoothindoorpositioningmethodusingdynamicfingerprintwindow
AT zhangling improvedbluetoothindoorpositioningmethodusingdynamicfingerprintwindow
AT zhoutong improvedbluetoothindoorpositioningmethodusingdynamicfingerprintwindow
AT longyi improvedbluetoothindoorpositioningmethodusingdynamicfingerprintwindow