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Deep neural network-based fusion localization using smartphones

Indoor location-based services (LBS) have tremendous practical and social value in intelligent life due to the pervasiveness of smartphones. The magnetic field-based localization method has been an interesting research hotspot because of its temporal stability, ubiquitousness, infrastructure-free na...

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Main Authors:	Yan, Suqing, Su, Yalan, Xiao, Jianming, Luo, Xiaonan, Ji, Yuanfa, Kamarul Hawari, Ghazali
Format:	Article
Language:	English
Published:	MDPI 2023
Subjects:	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/42854/1/Deep%20neural%20network-based%20fusion%20localization%20using%20smartphones.pdf http://umpir.ump.edu.my/id/eprint/42854/ https://doi.org/10.3390/s23218680 https://doi.org/10.3390/s23218680
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id	my.ump.umpir.42854
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spelling	my.ump.umpir.428542025-01-07T04:53:48Z http://umpir.ump.edu.my/id/eprint/42854/ Deep neural network-based fusion localization using smartphones Yan, Suqing Su, Yalan Xiao, Jianming Luo, Xiaonan Ji, Yuanfa Kamarul Hawari, Ghazali T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Indoor location-based services (LBS) have tremendous practical and social value in intelligent life due to the pervasiveness of smartphones. The magnetic field-based localization method has been an interesting research hotspot because of its temporal stability, ubiquitousness, infrastructure-free nature, and good compatibility with smartphones. However, utilizing discrete magnetic signals may result in ambiguous localization features caused by random noise and similar magnetic signals in complex symmetric and large-scale indoor environments. To address this issue, we propose a deep neural network-based fusion indoor localization system that integrates magnetic and pedestrian dead reckoning (PDR). In this system, we first propose a ResNet-GRU-LSTM neural network model to achieve magnetic localization more accurately. Afterward, we put forward a multifeatured-driven step length estimation. A hierarchy GRU (H-GRU) neural network model is proposed, and a multidimensional dataset using acceleration and a gyroscope is constructed to extract more valid characteristics. Finally, more reliable and accurate pedestrian localization can be achieved under the particle filter framework. Experiments were conducted at two trial sites with two pedestrians and four smartphones. Results demonstrate that the proposed system achieves better accuracy and robustness than other traditional localization algorithms. Moreover, the proposed system exhibits good generality and practicality in real-time localization with low cost and low computational complexity. MDPI 2023-10-24 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/42854/1/Deep%20neural%20network-based%20fusion%20localization%20using%20smartphones.pdf Yan, Suqing and Su, Yalan and Xiao, Jianming and Luo, Xiaonan and Ji, Yuanfa and Kamarul Hawari, Ghazali (2023) Deep neural network-based fusion localization using smartphones. Sensors (Basel, Switzerland), 23 (21). pp. 1-29. ISSN 1424-8220. (Published) https://doi.org/10.3390/s23218680 https://doi.org/10.3390/s23218680
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
building	UMPSA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang Al-Sultan Abdullah
content_source	UMPSA Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English
topic	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Yan, Suqing Su, Yalan Xiao, Jianming Luo, Xiaonan Ji, Yuanfa Kamarul Hawari, Ghazali Deep neural network-based fusion localization using smartphones
description	Indoor location-based services (LBS) have tremendous practical and social value in intelligent life due to the pervasiveness of smartphones. The magnetic field-based localization method has been an interesting research hotspot because of its temporal stability, ubiquitousness, infrastructure-free nature, and good compatibility with smartphones. However, utilizing discrete magnetic signals may result in ambiguous localization features caused by random noise and similar magnetic signals in complex symmetric and large-scale indoor environments. To address this issue, we propose a deep neural network-based fusion indoor localization system that integrates magnetic and pedestrian dead reckoning (PDR). In this system, we first propose a ResNet-GRU-LSTM neural network model to achieve magnetic localization more accurately. Afterward, we put forward a multifeatured-driven step length estimation. A hierarchy GRU (H-GRU) neural network model is proposed, and a multidimensional dataset using acceleration and a gyroscope is constructed to extract more valid characteristics. Finally, more reliable and accurate pedestrian localization can be achieved under the particle filter framework. Experiments were conducted at two trial sites with two pedestrians and four smartphones. Results demonstrate that the proposed system achieves better accuracy and robustness than other traditional localization algorithms. Moreover, the proposed system exhibits good generality and practicality in real-time localization with low cost and low computational complexity.
format	Article
author	Yan, Suqing Su, Yalan Xiao, Jianming Luo, Xiaonan Ji, Yuanfa Kamarul Hawari, Ghazali
author_facet	Yan, Suqing Su, Yalan Xiao, Jianming Luo, Xiaonan Ji, Yuanfa Kamarul Hawari, Ghazali
author_sort	Yan, Suqing
title	Deep neural network-based fusion localization using smartphones
title_short	Deep neural network-based fusion localization using smartphones
title_full	Deep neural network-based fusion localization using smartphones
title_fullStr	Deep neural network-based fusion localization using smartphones
title_full_unstemmed	Deep neural network-based fusion localization using smartphones
title_sort	deep neural network-based fusion localization using smartphones
publisher	MDPI
publishDate	2023
url	http://umpir.ump.edu.my/id/eprint/42854/1/Deep%20neural%20network-based%20fusion%20localization%20using%20smartphones.pdf http://umpir.ump.edu.my/id/eprint/42854/ https://doi.org/10.3390/s23218680 https://doi.org/10.3390/s23218680
_version_	1822924890751107072
score	13.232406

Deep neural network-based fusion localization using smartphones

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