Staff View: Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning

Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning

Accurate location information has significant commercial and economic value as they are widely used in intelligent manufacturing, material localization and smart homes. Magnetic sequence-based approaches show great promise mainly due to their pervasiveness and stability. However, existing geomagneti...

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Main Authors:	Yan, Suqing, Su, Yalan, Luo, Xiaonan, Sun, Anqing, Ji, Yuanfa, Kamarul Hawari, Ghazali
Format:	Article
Language:	English
Published:	MDPI AG, Basel, Switzerland 2023
Subjects:	TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/38908/1/Deep%20Learning-Based%20Geomagnetic%20Navigation%20Method%20Integrated%20with%20Dead%20Reckoning.pdf http://umpir.ump.edu.my/id/eprint/38908/ https://doi.org/10.3390/rs15174165
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id	my.ump.umpir.38908
record_format	eprints
spelling	my.ump.umpir.389082023-10-17T04:34:16Z http://umpir.ump.edu.my/id/eprint/38908/ Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning Yan, Suqing Su, Yalan Luo, Xiaonan Sun, Anqing Ji, Yuanfa Kamarul Hawari, Ghazali TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Accurate location information has significant commercial and economic value as they are widely used in intelligent manufacturing, material localization and smart homes. Magnetic sequence-based approaches show great promise mainly due to their pervasiveness and stability. However, existing geomagnetic indoor localization methods are facing the problems of location ambiguity and feature extraction deficiency, which will lead to large localization errors. To address these issues, we propose a coarse-to-fine geomagnetic indoor localization method based on deep learning. First, a multidimensional geomagnetic feature extraction method is presented which can extract magnetic features from spatial and temporal aspects. Then, a hierarchical deep neural network model is devised to extract more accurate geomagnetic information and corresponding location clues for more accurate localization. Finally, localization is achieved through a particle filter combined with IMU localization. To evaluate the performance of the proposed methods, we carried out several experiments at three trial paths with two heterogeneous devices, Vivo X30 and Huawei Mate30. Experimental results demonstrate that the proposed algorithm can achieve more accurate localization performance than the state-of-the-art methods. Meanwhile, the proposed algorithm has low cost and good pervasiveness for different devices. MDPI AG, Basel, Switzerland 2023 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/38908/1/Deep%20Learning-Based%20Geomagnetic%20Navigation%20Method%20Integrated%20with%20Dead%20Reckoning.pdf Yan, Suqing and Su, Yalan and Luo, Xiaonan and Sun, Anqing and Ji, Yuanfa and Kamarul Hawari, Ghazali (2023) Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning. Remote Sensing, 25 (7). pp. 1-25. ISSN 2072-4292. (Published) https://doi.org/10.3390/rs15174165 10.3390/rs15174165
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	TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Yan, Suqing Su, Yalan Luo, Xiaonan Sun, Anqing Ji, Yuanfa Kamarul Hawari, Ghazali Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning
description	Accurate location information has significant commercial and economic value as they are widely used in intelligent manufacturing, material localization and smart homes. Magnetic sequence-based approaches show great promise mainly due to their pervasiveness and stability. However, existing geomagnetic indoor localization methods are facing the problems of location ambiguity and feature extraction deficiency, which will lead to large localization errors. To address these issues, we propose a coarse-to-fine geomagnetic indoor localization method based on deep learning. First, a multidimensional geomagnetic feature extraction method is presented which can extract magnetic features from spatial and temporal aspects. Then, a hierarchical deep neural network model is devised to extract more accurate geomagnetic information and corresponding location clues for more accurate localization. Finally, localization is achieved through a particle filter combined with IMU localization. To evaluate the performance of the proposed methods, we carried out several experiments at three trial paths with two heterogeneous devices, Vivo X30 and Huawei Mate30. Experimental results demonstrate that the proposed algorithm can achieve more accurate localization performance than the state-of-the-art methods. Meanwhile, the proposed algorithm has low cost and good pervasiveness for different devices.
format	Article
author	Yan, Suqing Su, Yalan Luo, Xiaonan Sun, Anqing Ji, Yuanfa Kamarul Hawari, Ghazali
author_facet	Yan, Suqing Su, Yalan Luo, Xiaonan Sun, Anqing Ji, Yuanfa Kamarul Hawari, Ghazali
author_sort	Yan, Suqing
title	Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning
title_short	Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning
title_full	Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning
title_fullStr	Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning
title_full_unstemmed	Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning
title_sort	deep learning-based geomagnetic navigation method integrated with dead reckoning
publisher	MDPI AG, Basel, Switzerland
publishDate	2023
url	http://umpir.ump.edu.my/id/eprint/38908/1/Deep%20Learning-Based%20Geomagnetic%20Navigation%20Method%20Integrated%20with%20Dead%20Reckoning.pdf http://umpir.ump.edu.my/id/eprint/38908/ https://doi.org/10.3390/rs15174165
_version_	1822923778935488512
score	13.232413

Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning

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