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.
Full text not available from this repository.Abstract
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.
| Item Type: | Article |
|---|---|
| Funders: | National Natural Science Foundation of China (NSFC) |
| Uncontrolled Keywords: | Pedestrians; Data integration; Sensors; Hidden Markov models; Internet of Things; Inertial navigation; Hardware; Body sensor network; deep learning; inertial measurement unit; multisensor data fusion; zero velocity update (ZUPT) |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 06 Nov 2024 05:09 |
| Last Modified: | 06 Nov 2024 05:09 |
| URI: | http://eprints.um.edu.my/id/eprint/45618 |
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