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An improved PDR system with accurate heading and step length estimation using handheld smartphone

Published online by Cambridge University Press:  30 July 2021

Dayu Yan Open the ORCID record for Dayu Yan [Opens in a new window] ,
Chuang Shi  and
Tuan Li
Dayu Yan
Affiliation:
Beihang University School of Electronic and Information Engineering, Beijing 100083, China
Chuang Shi
Affiliation:
School of Electronic and Information Engineering, Beihang University, Beijing, China
Tuan Li*
Affiliation:
Beihang University School of Electronic and Information Engineering, Beijing 100083, China
*
*Corresponding author. E-mail: tuanli@whu.edu.cn
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Abstract

Pedestrian dead reckoning (PDR) is widely used in handheld indoor positioning systems. However, low-cost inertial sensors built into smartphones provide poor-quality measurements, resulting in cumulative error which consists of heading estimation error caused by gyroscope and step length estimation error caused by an accelerometer. Learning more motion features through limited measurements is important to improve positioning accuracy. This paper proposes an improved PDR system using smartphone sensors. Using gyroscope, two motion patterns, walking straight or turning, can be recognised based on dynamic time warp (DTW) and thus improve heading estimation from an extended Kalman filter (EKF). Joint quasi-static field (JQSF) detection is used to avoid bad magnetic measurements due to magnetic disturbances in an indoor environment. In terms of periodicity of angular rate while walking, peak–valley angular velocity detection and zero-cross detection is combined to detect steps. A step-length estimation method based on deep belief network (DBN) is proposed. Experimental results demonstrate that the proposed PDR system can achieve more accurate indoor positioning.

Keywords

Indoor positioningpedestrian dead reckoning (PDR)Kalman filterdeep belief networksmartphone

Information

Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 1 , January 2022 , pp. 141 - 159
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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