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mPILOT-Magnetic Field Strength Based Pedestrian Indoor Localization

An indoor localization system based on off-the-shelf smartphone sensors is presented which employs the magnetometer to find user location. Further assisted by the accelerometer and gyroscope, the proposed system is able to locate the user without any prior knowledge of user initial position. The sys...

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Detalles Bibliográficos
Autores principales: Ashraf, Imran, Hur, Soojung, Park, Yongwan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6068652/
https://www.ncbi.nlm.nih.gov/pubmed/30011927
http://dx.doi.org/10.3390/s18072283
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author Ashraf, Imran
Hur, Soojung
Park, Yongwan
author_facet Ashraf, Imran
Hur, Soojung
Park, Yongwan
author_sort Ashraf, Imran
collection PubMed
description An indoor localization system based on off-the-shelf smartphone sensors is presented which employs the magnetometer to find user location. Further assisted by the accelerometer and gyroscope, the proposed system is able to locate the user without any prior knowledge of user initial position. The system exploits the fingerprint database approach for localization. Traditional fingerprinting technology stores data intensity values in database such as RSSI (Received Signal Strength Indicator) values in the case of WiFi fingerprinting and magnetic flux intensity values in the case of geomagnetic fingerprinting. The down side is the need to update the database periodically and device heterogeneity. We solve this problem by using the fingerprint database of patterns formed by magnetic flux intensity values. The pattern matching approach solves the problem of device heterogeneity and the algorithm’s performance with Samsung Galaxy S8 and LG G6 is comparable. A deep learning based artificial neural network is adopted to identify the user state of walking and stationary and its accuracy is 95%. The localization is totally infrastructure independent and does not require any other technology to constraint the search space. The experiments are performed to determine the accuracy in three buildings of Yeungnam University, Republic of Korea with different path lengths and path geometry. The results demonstrate that the error is 2–3 m for 50 percentile with various buildings. Even though many locations in the same building exhibit very similar magnetic attitude, the algorithm achieves an accuracy of 4 m for 75 percentile irrespective of the device used for localization.
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spelling pubmed-60686522018-08-07 mPILOT-Magnetic Field Strength Based Pedestrian Indoor Localization Ashraf, Imran Hur, Soojung Park, Yongwan Sensors (Basel) Article An indoor localization system based on off-the-shelf smartphone sensors is presented which employs the magnetometer to find user location. Further assisted by the accelerometer and gyroscope, the proposed system is able to locate the user without any prior knowledge of user initial position. The system exploits the fingerprint database approach for localization. Traditional fingerprinting technology stores data intensity values in database such as RSSI (Received Signal Strength Indicator) values in the case of WiFi fingerprinting and magnetic flux intensity values in the case of geomagnetic fingerprinting. The down side is the need to update the database periodically and device heterogeneity. We solve this problem by using the fingerprint database of patterns formed by magnetic flux intensity values. The pattern matching approach solves the problem of device heterogeneity and the algorithm’s performance with Samsung Galaxy S8 and LG G6 is comparable. A deep learning based artificial neural network is adopted to identify the user state of walking and stationary and its accuracy is 95%. The localization is totally infrastructure independent and does not require any other technology to constraint the search space. The experiments are performed to determine the accuracy in three buildings of Yeungnam University, Republic of Korea with different path lengths and path geometry. The results demonstrate that the error is 2–3 m for 50 percentile with various buildings. Even though many locations in the same building exhibit very similar magnetic attitude, the algorithm achieves an accuracy of 4 m for 75 percentile irrespective of the device used for localization. MDPI 2018-07-14 /pmc/articles/PMC6068652/ /pubmed/30011927 http://dx.doi.org/10.3390/s18072283 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
Ashraf, Imran
Hur, Soojung
Park, Yongwan
mPILOT-Magnetic Field Strength Based Pedestrian Indoor Localization
title mPILOT-Magnetic Field Strength Based Pedestrian Indoor Localization
title_full mPILOT-Magnetic Field Strength Based Pedestrian Indoor Localization
title_fullStr mPILOT-Magnetic Field Strength Based Pedestrian Indoor Localization
title_full_unstemmed mPILOT-Magnetic Field Strength Based Pedestrian Indoor Localization
title_short mPILOT-Magnetic Field Strength Based Pedestrian Indoor Localization
title_sort mpilot-magnetic field strength based pedestrian indoor localization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6068652/
https://www.ncbi.nlm.nih.gov/pubmed/30011927
http://dx.doi.org/10.3390/s18072283
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