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Multiple Fingerprinting Localization by an Artificial Neural Network

Fingerprinting localization is a promising indoor positioning methods thanks to its advantage of using preinstalled infrastructure. For example, WiFi signal strength can be measured by pre-existing WiFi routers. In the offline phase, the fingerprinting localization method first stores of position an...

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Autor principal: Yoo, Jaehyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573177/
https://www.ncbi.nlm.nih.gov/pubmed/36236604
http://dx.doi.org/10.3390/s22197505
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author Yoo, Jaehyun
author_facet Yoo, Jaehyun
author_sort Yoo, Jaehyun
collection PubMed
description Fingerprinting localization is a promising indoor positioning methods thanks to its advantage of using preinstalled infrastructure. For example, WiFi signal strength can be measured by pre-existing WiFi routers. In the offline phase, the fingerprinting localization method first stores of position and RSSI measurement pairs in a dataset. Second, it predicts a target’s location by comparing the stored fingerprint database to the current measurement. The database size is normally huge, and data patterns are complicated; thus, an artificial neural network is used to model the relationship of fingerprints and locations. The existing fingerprinting locations, however, have been developed to predict only single locations. In practice, many users may require positioning services, and as such, the core algorithm should be capable of multiple localizations, which is the main contribution of this paper. In this paper, multiple fingerprinting localization is developed based on an artificial neural network and an analysis of the number of targets that can be estimated without loss of accuracy is conducted by experiments.
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spelling pubmed-95731772022-10-17 Multiple Fingerprinting Localization by an Artificial Neural Network Yoo, Jaehyun Sensors (Basel) Communication Fingerprinting localization is a promising indoor positioning methods thanks to its advantage of using preinstalled infrastructure. For example, WiFi signal strength can be measured by pre-existing WiFi routers. In the offline phase, the fingerprinting localization method first stores of position and RSSI measurement pairs in a dataset. Second, it predicts a target’s location by comparing the stored fingerprint database to the current measurement. The database size is normally huge, and data patterns are complicated; thus, an artificial neural network is used to model the relationship of fingerprints and locations. The existing fingerprinting locations, however, have been developed to predict only single locations. In practice, many users may require positioning services, and as such, the core algorithm should be capable of multiple localizations, which is the main contribution of this paper. In this paper, multiple fingerprinting localization is developed based on an artificial neural network and an analysis of the number of targets that can be estimated without loss of accuracy is conducted by experiments. MDPI 2022-10-03 /pmc/articles/PMC9573177/ /pubmed/36236604 http://dx.doi.org/10.3390/s22197505 Text en © 2022 by the author. 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 Communication
Yoo, Jaehyun
Multiple Fingerprinting Localization by an Artificial Neural Network
title Multiple Fingerprinting Localization by an Artificial Neural Network
title_full Multiple Fingerprinting Localization by an Artificial Neural Network
title_fullStr Multiple Fingerprinting Localization by an Artificial Neural Network
title_full_unstemmed Multiple Fingerprinting Localization by an Artificial Neural Network
title_short Multiple Fingerprinting Localization by an Artificial Neural Network
title_sort multiple fingerprinting localization by an artificial neural network
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573177/
https://www.ncbi.nlm.nih.gov/pubmed/36236604
http://dx.doi.org/10.3390/s22197505
work_keys_str_mv AT yoojaehyun multiplefingerprintinglocalizationbyanartificialneuralnetwork