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Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting

Indoor signals are susceptible to NLOS propagation effects, multipath effects, and a dynamic environment, posing more challenges than outdoor signals despite decades of advancements in location services. In modern Wi-Fi networks that support both MIMO and OFDM techniques, Channel State Information (...

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Autores principales: Gidey, Hailu Tesfay, Guo, Xiansheng, Zhong, Ke, Li, Lin, Zhang, Yukun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9695974/
https://www.ncbi.nlm.nih.gov/pubmed/36433311
http://dx.doi.org/10.3390/s22228720
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author Gidey, Hailu Tesfay
Guo, Xiansheng
Zhong, Ke
Li, Lin
Zhang, Yukun
author_facet Gidey, Hailu Tesfay
Guo, Xiansheng
Zhong, Ke
Li, Lin
Zhang, Yukun
author_sort Gidey, Hailu Tesfay
collection PubMed
description Indoor signals are susceptible to NLOS propagation effects, multipath effects, and a dynamic environment, posing more challenges than outdoor signals despite decades of advancements in location services. In modern Wi-Fi networks that support both MIMO and OFDM techniques, Channel State Information (CSI) is now used as an enhanced wireless channel metric replacing the Wi-Fi received signal strength (RSS) fingerprinting method. The indoor multipath effects, however, make it less robust and stable. This study proposes a positive knowledge transfer-based heterogeneous data fusion method for representing the different scenarios of temporal variations in CSI-based fingerprint measurements generated in a complex indoor environment targeting indoor parking lots, while reducing the training calibration overhead. Extensive experiments were performed with real-world scenarios of the indoor parking phenomenon. Results revealed that the proposed algorithm proved to be an efficient algorithm with consistent positioning accuracy across all potential variations. In addition to improving indoor parking location accuracy, the proposed algorithm provides computationally robust and efficient location estimates in dynamic environments. A Cramer-Rao lower bound (CRLB) analysis was also used to estimate the lower bound of the parking lot location error variance under various temporal variation scenarios. Based on analytical derivations, we prove that the lower bound of the variance of the location estimator depends on the (i) angle of the base stations, (ii) number of base stations, (iii) distance between the target and the base station, [Formula: see text] (iv) correlation of the measurements, [Formula: see text] and (v) signal propagation parameters [Formula: see text] and [Formula: see text].
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spelling pubmed-96959742022-11-26 Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting Gidey, Hailu Tesfay Guo, Xiansheng Zhong, Ke Li, Lin Zhang, Yukun Sensors (Basel) Article Indoor signals are susceptible to NLOS propagation effects, multipath effects, and a dynamic environment, posing more challenges than outdoor signals despite decades of advancements in location services. In modern Wi-Fi networks that support both MIMO and OFDM techniques, Channel State Information (CSI) is now used as an enhanced wireless channel metric replacing the Wi-Fi received signal strength (RSS) fingerprinting method. The indoor multipath effects, however, make it less robust and stable. This study proposes a positive knowledge transfer-based heterogeneous data fusion method for representing the different scenarios of temporal variations in CSI-based fingerprint measurements generated in a complex indoor environment targeting indoor parking lots, while reducing the training calibration overhead. Extensive experiments were performed with real-world scenarios of the indoor parking phenomenon. Results revealed that the proposed algorithm proved to be an efficient algorithm with consistent positioning accuracy across all potential variations. In addition to improving indoor parking location accuracy, the proposed algorithm provides computationally robust and efficient location estimates in dynamic environments. A Cramer-Rao lower bound (CRLB) analysis was also used to estimate the lower bound of the parking lot location error variance under various temporal variation scenarios. Based on analytical derivations, we prove that the lower bound of the variance of the location estimator depends on the (i) angle of the base stations, (ii) number of base stations, (iii) distance between the target and the base station, [Formula: see text] (iv) correlation of the measurements, [Formula: see text] and (v) signal propagation parameters [Formula: see text] and [Formula: see text]. MDPI 2022-11-11 /pmc/articles/PMC9695974/ /pubmed/36433311 http://dx.doi.org/10.3390/s22228720 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gidey, Hailu Tesfay
Guo, Xiansheng
Zhong, Ke
Li, Lin
Zhang, Yukun
Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting
title Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting
title_full Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting
title_fullStr Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting
title_full_unstemmed Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting
title_short Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting
title_sort data fusion methods for indoor positioning systems based on channel state information fingerprinting
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9695974/
https://www.ncbi.nlm.nih.gov/pubmed/36433311
http://dx.doi.org/10.3390/s22228720
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