Learning-Based Stance Phase Detection and Multisensor Data Fusion for ZUPT-Aided Pedestrian Dead Reckoning System
Document Type
Article
Publication Date
2-1-2024
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.
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)
Divisions
ai
Funders
National Natural Science Foundation of China (NSFC)
Publication Title
IEEE Internet of Things Journal
Volume
11
Issue
4
Publisher
IEEE-Inst Electrical Electronics Engineers Inc
Publisher Location
445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA