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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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 |
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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 |
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