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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Main Authors: Xie, Feng, Xie, Ming, Wang, Cheng
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2023
Subjects:
Online Access:https://hdl.handle.net/10356/171697
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1716972023-11-11T16:48:27Z Using the MNL model in a mobile device's indoor positioning Xie, Feng Xie, Ming Wang, Cheng School of Mechanical and Aerospace Engineering Engineering::Mechanical engineering Mobile Devices 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 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). Published version The APC was funded by Sanda University, Shanghai, China. 2023-11-06T01:43:48Z 2023-11-06T01:43:48Z 2023 Journal Article Xie, F., Xie, M. & Wang, C. (2023). Using the MNL model in a mobile device's indoor positioning. Biomimetics, 8(2), 252-. https://dx.doi.org/10.3390/biomimetics8020252 2313-7673 https://hdl.handle.net/10356/171697 10.3390/biomimetics8020252 37366847 2-s2.0-85163851201 2 8 252 en Biomimetics © 2023 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 (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Mechanical engineering
Mobile Devices
Indoor Positioning
spellingShingle Engineering::Mechanical engineering
Mobile Devices
Indoor Positioning
Xie, Feng
Xie, Ming
Wang, Cheng
Using the MNL model in a mobile device's indoor positioning
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).
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Xie, Feng
Xie, Ming
Wang, Cheng
format Article
author Xie, Feng
Xie, Ming
Wang, Cheng
author_sort Xie, Feng
title 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_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_sort using the mnl model in a mobile device's indoor positioning
publishDate 2023
url https://hdl.handle.net/10356/171697
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