Using the MNL Model in a Mobile Device’s Indoor Positioning

Indoor Positioning Services (IPS) allow mobile devices or bionic robots to locate themselves quickly and accurately in large commercial complexes, shopping malls, supermarkets, exhibition venues, parking garages, airports, or train hubs, and access surrounding information. Wi-Fi-based indoor positio...

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Detalles Bibliográficos
Autores principales: Xie, Feng, Xie, Ming, Wang, Cheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10296597/
https://www.ncbi.nlm.nih.gov/pubmed/37366847
http://dx.doi.org/10.3390/biomimetics8020252
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author Xie, Feng
Xie, Ming
Wang, Cheng
author_facet Xie, Feng
Xie, Ming
Wang, Cheng
author_sort Xie, Feng
collection PubMed
description Indoor Positioning Services (IPS) allow mobile devices or bionic robots to locate themselves quickly and accurately in large commercial complexes, shopping malls, supermarkets, exhibition venues, parking garages, airports, or train hubs, and access surrounding information. Wi-Fi-based indoor positioning technology can use existing WLAN networks, and has promising prospects for broad market applications. This paper presents a method using the Multinomial Logit Model (MNL) to generate Wi-Fi signal fingerprints for positioning in real time. In an experiment, 31 locations were randomly selected and tested to validate the model, showing mobile devices could determine their locations with an accuracy of around 3 m (2.53 m median).
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spelling pubmed-102965972023-06-28 Using the MNL Model in a Mobile Device’s Indoor Positioning Xie, Feng Xie, Ming Wang, Cheng Biomimetics (Basel) Article Indoor Positioning Services (IPS) allow mobile devices or bionic robots to locate themselves quickly and accurately in large commercial complexes, shopping malls, supermarkets, exhibition venues, parking garages, airports, or train hubs, and access surrounding information. Wi-Fi-based indoor positioning technology can use existing WLAN networks, and has promising prospects for broad market applications. This paper presents a method using the Multinomial Logit Model (MNL) to generate Wi-Fi signal fingerprints for positioning in real time. In an experiment, 31 locations were randomly selected and tested to validate the model, showing mobile devices could determine their locations with an accuracy of around 3 m (2.53 m median). MDPI 2023-06-13 /pmc/articles/PMC10296597/ /pubmed/37366847 http://dx.doi.org/10.3390/biomimetics8020252 Text en © 2023 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
Xie, Feng
Xie, Ming
Wang, Cheng
Using the MNL Model in a Mobile Device’s Indoor Positioning
title Using the MNL Model in a Mobile Device’s Indoor Positioning
title_full Using the MNL Model in a Mobile Device’s Indoor Positioning
title_fullStr Using the MNL Model in a Mobile Device’s Indoor Positioning
title_full_unstemmed Using the MNL Model in a Mobile Device’s Indoor Positioning
title_short Using the MNL Model in a Mobile Device’s Indoor Positioning
title_sort using the mnl model in a mobile device’s indoor positioning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10296597/
https://www.ncbi.nlm.nih.gov/pubmed/37366847
http://dx.doi.org/10.3390/biomimetics8020252
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