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Learning-Based Stance Phase Detection and Multisensor Data Fusion for ZUPT-Aided Pedestrian Dead Reckoning System

In a closed environment lacking global positioning system (GPS) signals, how to achieve accurate navigation and positioning is a very challenging task. Zero velocity update (ZUPT) is a highly effective foot-mounted inertial pedestrian navigation systems in such environment. However, despite its effe...

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Main Authors: Li, Jie, Zhou, Xu, Qiu, Sen, Mao, Yi, Wang, Ziyang, Loo, Chu Kiong, Liu, Xiaofeng
Format: Article
Published: IEEE-Inst Electrical Electronics Engineers Inc 2024
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.um.edu.my/45618/
https://doi.org/10.1109/JIOT.2023.3308100
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spelling my.um.eprints.456182024-11-06T05:09:41Z http://eprints.um.edu.my/45618/ Learning-Based Stance Phase Detection and Multisensor Data Fusion for ZUPT-Aided Pedestrian Dead Reckoning System Li, Jie Zhou, Xu Qiu, Sen Mao, Yi Wang, Ziyang Loo, Chu Kiong Liu, Xiaofeng QA75 Electronic computers. Computer science In a closed environment lacking global positioning system (GPS) signals, how to achieve accurate navigation and positioning is a very challenging task. Zero velocity update (ZUPT) is a highly effective foot-mounted inertial pedestrian navigation systems in such environment. However, despite its effectiveness, the limitation of accurate detecting the zero-velocity-interval (ZVI) and heading drift are still the significant challenges of the ZUPT method. To address these issues, a deep learning method for adaptive ZVIs detection is established based solely on inertial sensors by comparing with the optical motion capture system. Additionally, an improved ZUPT-aided extend Kalman filter (EKF) divides the measurement updates of the ZVIs is established for multisensor data fusion, and the heading change with heuristic drift reduction (HDR) is also adopt as measurement, thereby yielding to limit the heading drift. Experimental results demonstrate that our method provides a better estimate of the heading angle, as well as more accurate ZVIs detection, leading to more precise dead-reckoning position estimates than other state-of-the-art methods. IEEE-Inst Electrical Electronics Engineers Inc 2024-02 Article PeerReviewed Li, Jie and Zhou, Xu and Qiu, Sen and Mao, Yi and Wang, Ziyang and Loo, Chu Kiong and Liu, Xiaofeng (2024) Learning-Based Stance Phase Detection and Multisensor Data Fusion for ZUPT-Aided Pedestrian Dead Reckoning System. IEEE Internet of Things Journal, 11 (4). pp. 5899-5911. ISSN 2327-4662, DOI https://doi.org/10.1109/JIOT.2023.3308100 <https://doi.org/10.1109/JIOT.2023.3308100>. https://doi.org/10.1109/JIOT.2023.3308100 10.1109/JIOT.2023.3308100
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Li, Jie
Zhou, Xu
Qiu, Sen
Mao, Yi
Wang, Ziyang
Loo, Chu Kiong
Liu, Xiaofeng
Learning-Based Stance Phase Detection and Multisensor Data Fusion for ZUPT-Aided Pedestrian Dead Reckoning System
description In a closed environment lacking global positioning system (GPS) signals, how to achieve accurate navigation and positioning is a very challenging task. Zero velocity update (ZUPT) is a highly effective foot-mounted inertial pedestrian navigation systems in such environment. However, despite its effectiveness, the limitation of accurate detecting the zero-velocity-interval (ZVI) and heading drift are still the significant challenges of the ZUPT method. To address these issues, a deep learning method for adaptive ZVIs detection is established based solely on inertial sensors by comparing with the optical motion capture system. Additionally, an improved ZUPT-aided extend Kalman filter (EKF) divides the measurement updates of the ZVIs is established for multisensor data fusion, and the heading change with heuristic drift reduction (HDR) is also adopt as measurement, thereby yielding to limit the heading drift. Experimental results demonstrate that our method provides a better estimate of the heading angle, as well as more accurate ZVIs detection, leading to more precise dead-reckoning position estimates than other state-of-the-art methods.
format Article
author Li, Jie
Zhou, Xu
Qiu, Sen
Mao, Yi
Wang, Ziyang
Loo, Chu Kiong
Liu, Xiaofeng
author_facet Li, Jie
Zhou, Xu
Qiu, Sen
Mao, Yi
Wang, Ziyang
Loo, Chu Kiong
Liu, Xiaofeng
author_sort Li, Jie
title Learning-Based Stance Phase Detection and Multisensor Data Fusion for ZUPT-Aided Pedestrian Dead Reckoning System
title_short Learning-Based Stance Phase Detection and Multisensor Data Fusion for ZUPT-Aided Pedestrian Dead Reckoning System
title_full Learning-Based Stance Phase Detection and Multisensor Data Fusion for ZUPT-Aided Pedestrian Dead Reckoning System
title_fullStr Learning-Based Stance Phase Detection and Multisensor Data Fusion for ZUPT-Aided Pedestrian Dead Reckoning System
title_full_unstemmed Learning-Based Stance Phase Detection and Multisensor Data Fusion for ZUPT-Aided Pedestrian Dead Reckoning System
title_sort learning-based stance phase detection and multisensor data fusion for zupt-aided pedestrian dead reckoning system
publisher IEEE-Inst Electrical Electronics Engineers Inc
publishDate 2024
url http://eprints.um.edu.my/45618/
https://doi.org/10.1109/JIOT.2023.3308100
_version_ 1816130427575861248
score 13.222552

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