Meng, X, Zhang, Z-Q orcid.org/0000-0003-0204-3867, Wu, J-K et al. (2 more authors) (2014) Self-Contained Pedestrian Tracking During Normal Walking Using an Inertial/Magnetic Sensor Module. IEEE Transactions on Biomedical Engineering, 61 (3). pp. 892-899. ISSN 0018-9294
Abstract
This paper proposes a novel self-contained pedestrian tracking method using a foot-mounted inertial and magnetic sensor module, which not only uses the traditional zero velocity updates, but also applies the stride information to further correct the acceleration double integration drifts and thus improves the tracking accuracy. In our method, a velocity control variable is designed in the process model, which is set to the average velocity derived from stride information in the swing (nonzero velocity) phases or zero in the stance (zero-velocity) phases. Stride-based position information is also derived as the pseudomeasurements to further improve the accuracy of the position estimates. An adaptive Kalman filter is then designed to fuse all the sensor information and pseudomeasurements. The proposed pedestrian tracking method has been extensively evaluated using experiments, including both short distance walking with different patterns and long distance walking performed indoors and outdoors, and have been shown to perform effectively for pedestrian tracking.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2013 IEEE. This is an author produced version of a paper published in IEEE Transactions on Biomedical Engineering. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Pedestrian navigation, sensor fusion, stride counting, unscented Kalman filter (UKF), zero velocity update |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Jun 2016 15:52 |
Last Modified: | 28 Oct 2020 15:37 |
Published Version: | http://dx.doi.org/10.1109/TBME.2013.2291910 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TBME.2013.2291910 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:98817 |