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...

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Autores principales: Wang, Yanzhao, Xiu, Chundi, Zhang, Xuanli, Yang, Dongkai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
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.
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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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