WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest
WiFi fingerprinting indoor positioning systems have extensive applied prospects. However, a vast amount of data in a particular environment has to be gathered to establish a fingerprinting database. Deficiencies of these systems are the lack of universality of multipath effects and a burden of heavy...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6164737/ https://www.ncbi.nlm.nih.gov/pubmed/30200285 http://dx.doi.org/10.3390/s18092869 |
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author | Wang, Yanzhao Xiu, Chundi Zhang, Xuanli Yang, Dongkai |
author_facet | Wang, Yanzhao Xiu, Chundi Zhang, Xuanli Yang, Dongkai |
author_sort | Wang, Yanzhao |
collection | PubMed |
description | WiFi fingerprinting indoor positioning systems have extensive applied prospects. However, a vast amount of data in a particular environment has to be gathered to establish a fingerprinting database. Deficiencies of these systems are the lack of universality of multipath effects and a burden of heavy workload on fingerprint storage. Thus, this paper presents a novel Random Forest fingerprinting localization (RFFP) method using channel state information (CSI), which utilizes the Random Forest model trained in the offline stage as fingerprints in order to economize memory space and possess a good anti-multipath characteristic. Furthermore, a series of specific experiments are conducted in a microwave anechoic chamber and an office to detail the localization performance of RFFP with different wireless channel circumstances, system parameters, algorithms, and input datasets. In addition, compared with other algorithms including K-Nearest-Neighbor (KNN), Weighted K-Nearest-Neighbor (WKNN), REPTree, CART, and J48, the RFFP method provides far greater classification accuracy as well as lower mean location error. The proposed method offers outstanding comprehensive performance including accuracy, robustness, low workload, and better anti-multipath-fading. |
format | Online Article Text |
id | pubmed-6164737 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-61647372018-10-10 WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest Wang, Yanzhao Xiu, Chundi Zhang, Xuanli Yang, Dongkai Sensors (Basel) Article WiFi fingerprinting indoor positioning systems have extensive applied prospects. However, a vast amount of data in a particular environment has to be gathered to establish a fingerprinting database. Deficiencies of these systems are the lack of universality of multipath effects and a burden of heavy workload on fingerprint storage. Thus, this paper presents a novel Random Forest fingerprinting localization (RFFP) method using channel state information (CSI), which utilizes the Random Forest model trained in the offline stage as fingerprints in order to economize memory space and possess a good anti-multipath characteristic. Furthermore, a series of specific experiments are conducted in a microwave anechoic chamber and an office to detail the localization performance of RFFP with different wireless channel circumstances, system parameters, algorithms, and input datasets. In addition, compared with other algorithms including K-Nearest-Neighbor (KNN), Weighted K-Nearest-Neighbor (WKNN), REPTree, CART, and J48, the RFFP method provides far greater classification accuracy as well as lower mean location error. The proposed method offers outstanding comprehensive performance including accuracy, robustness, low workload, and better anti-multipath-fading. MDPI 2018-08-31 /pmc/articles/PMC6164737/ /pubmed/30200285 http://dx.doi.org/10.3390/s18092869 Text en © 2018 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 Wang, Yanzhao Xiu, Chundi Zhang, Xuanli Yang, Dongkai WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest |
title | WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest |
title_full | WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest |
title_fullStr | WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest |
title_full_unstemmed | WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest |
title_short | WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest |
title_sort | wifi indoor localization with csi fingerprinting-based random forest |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6164737/ https://www.ncbi.nlm.nih.gov/pubmed/30200285 http://dx.doi.org/10.3390/s18092869 |
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