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Sensors 2015, 15(3), 5032-5057; doi:10.3390/s150305032

Vector Graph Assisted Pedestrian Dead Reckoning Using an Unconstrained Smartphone

Shanghai Key Laboratory of Navigation and Location-based Services, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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Academic Editor: Kourosh Khoshelham
Received: 3 December 2014 / Revised: 7 February 2015 / Accepted: 15 February 2015 / Published: 2 March 2015
(This article belongs to the Special Issue Sensors for Indoor Mapping and Navigation)

Abstract

The paper presents a hybrid indoor positioning solution based on a pedestrian dead reckoning (PDR) approach using built-in sensors on a smartphone. To address the challenges of flexible and complex contexts of carrying a phone while walking, a robust step detection algorithm based on motion-awareness has been proposed. Given the fact that step length is influenced by different motion states, an adaptive step length estimation algorithm based on motion recognition is developed. Heading estimation is carried out by an attitude acquisition algorithm, which contains a two-phase filter to mitigate the distortion of magnetic anomalies. In order to estimate the heading for an unconstrained smartphone, principal component analysis (PCA) of acceleration is applied to determine the offset between the orientation of smartphone and the actual heading of a pedestrian. Moreover, a particle filter with vector graph assisted particle weighting is introduced to correct the deviation in step length and heading estimation. Extensive field tests, including four contexts of carrying a phone, have been conducted in an office building to verify the performance of the proposed algorithm. Test results show that the proposed algorithm can achieve sub-meter mean error in all contexts. View Full-Text
Keywords: indoor localization; pedestrian dead reckoning; principal component analysis; particle filter; vector graph; smartphone sensors indoor localization; pedestrian dead reckoning; principal component analysis; particle filter; vector graph; smartphone sensors
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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

Qian, J.; Pei, L.; Ma, J.; Ying, R.; Liu, P. Vector Graph Assisted Pedestrian Dead Reckoning Using an Unconstrained Smartphone. Sensors 2015, 15, 5032-5057.

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