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Sensors 2017, 17(11), 2695; https://doi.org/10.3390/s17112695

A Novel 3D Pedestrian Navigation Method for a Multiple Sensors-Based Foot-Mounted Inertial System

School of Electronic and Information Engineering, Beihang University, Beijing 100083, China
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Author to whom correspondence should be addressed.
Received: 19 September 2017 / Revised: 3 November 2017 / Accepted: 17 November 2017 / Published: 22 November 2017
(This article belongs to the Section Physical Sensors)

Abstract

In this paper, we present a novel method for 3D pedestrian navigation of foot-mounted inertial systems by integrating a MEMS-IMU, barometer, and permanent magnet. Zero-velocity update (ZUPT) is a well-known algorithm to eliminate the accumulated error of foot-mounted inertial systems. However, the ZUPT stance phase detector using acceleration and angular rate is threshold-based, which may cause incorrect stance phase estimation in the running gait pattern. A permanent magnet-based ZUPT detector is introduced to solve this problem. Peaks extracted from the magnetic field strength waveform are mid-stances of stance phases. A model of peak-peak information and stance phase duration is developed to have a quantitative calculation method of stance phase duration in different movement patterns. Height estimation using barometer is susceptible to the environment. A height difference information aided barometer (HDIB) algorithm integrating MEMS-IMU and barometer is raised to have a better height estimation. The first stage of HDIB is to distinguish level ground/upstairs/downstairs and the second stage is to calculate height using reference atmospheric pressure obtained from the first stage. At last, a ZUPT-based adaptive average window length algorithm (ZUPT-AAWL) is proposed to calculate the true total travelled distance to have a more accurate percentage error (TTDE). This proposed method is verified via multiple experiments. Numerical results show that TTDE ranges from 0.32% to 1.04% in both walking and running gait patterns, and the height estimation error is from 0 m to 2.35 m. View Full-Text
Keywords: indoor positioning; MEMS-IMU; barometer; permanent magnet; zero-velocity update; height estimation; magnetic field strength; extended Kalman filter indoor positioning; MEMS-IMU; barometer; permanent magnet; zero-velocity update; height estimation; magnetic field strength; extended Kalman filter
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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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Yang, W.; Xiu, C.; Zhang, J.; Yang, D. A Novel 3D Pedestrian Navigation Method for a Multiple Sensors-Based Foot-Mounted Inertial System. Sensors 2017, 17, 2695.

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