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Sensors 2016, 16(5), 677; doi:10.3390/s16050677

Carrying Position Independent User Heading Estimation for Indoor Pedestrian Navigation with Smartphones

1
School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
2
School of computer science and technology, Harbin Institute of Technology, Harbin 150001, China
*
Author to whom correspondence should be addressed.
Academic Editor: Jörg F. Wagner
Received: 20 March 2016 / Revised: 4 May 2016 / Accepted: 4 May 2016 / Published: 11 May 2016
(This article belongs to the Special Issue Inertial Sensors and Systems 2016)
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Abstract

This paper proposes a novel heading estimation approach for indoor pedestrian navigation using the built-in inertial sensors on a smartphone. Unlike previous approaches constraining the carrying position of a smartphone on the user’s body, our approach gives the user a larger freedom by implementing automatic recognition of the device carrying position and subsequent selection of an optimal strategy for heading estimation. We firstly predetermine the motion state by a decision tree using an accelerometer and a barometer. Then, to enable accurate and computational lightweight carrying position recognition, we combine a position classifier with a novel position transition detection algorithm, which may also be used to avoid the confusion between position transition and user turn during pedestrian walking. For a device placed in the trouser pockets or held in a swinging hand, the heading estimation is achieved by deploying a principal component analysis (PCA)-based approach. For a device held in the hand or against the ear during a phone call, user heading is directly estimated by adding the yaw angle of the device to the related heading offset. Experimental results show that our approach can automatically detect carrying positions with high accuracy, and outperforms previous heading estimation approaches in terms of accuracy and applicability. View Full-Text
Keywords: heading estimation; carrying position; inertial sensors; principal component analysis heading estimation; carrying position; inertial sensors; principal component analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Deng, Z.-A.; Wang, G.; Hu, Y.; Cui, Y. Carrying Position Independent User Heading Estimation for Indoor Pedestrian Navigation with Smartphones. Sensors 2016, 16, 677.

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