A Novel 3D Pedestrian Navigation Method for a Multiple Sensors-Based Foot-Mounted Inertial System
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 Z...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/17/11/2695 |
| _version_ | 1811305564237463552 |
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| author | Wei Yang Chundi Xiu Jianmin Zhang Dongkai Yang |
| author_facet | Wei Yang Chundi Xiu Jianmin Zhang Dongkai Yang |
| author_sort | Wei Yang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T08:28:45Z |
| format | Article |
| id | doaj.art-3519a39cba4744a385abe9a6f7e296fa |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:28:45Z |
| publishDate | 2017-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3519a39cba4744a385abe9a6f7e296fa2022-12-22T02:54:20ZengMDPI AGSensors1424-82202017-11-011711269510.3390/s17112695s17112695A Novel 3D Pedestrian Navigation Method for a Multiple Sensors-Based Foot-Mounted Inertial SystemWei Yang0Chundi Xiu1Jianmin Zhang2Dongkai Yang3School of Electronic and Information Engineering, Beihang University, Beijing 100083, ChinaSchool of Electronic and Information Engineering, Beihang University, Beijing 100083, ChinaSchool of Electronic and Information Engineering, Beihang University, Beijing 100083, ChinaSchool of Electronic and Information Engineering, Beihang University, Beijing 100083, ChinaIn 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.https://www.mdpi.com/1424-8220/17/11/2695indoor positioningMEMS-IMUbarometerpermanent magnetzero-velocity updateheight estimationmagnetic field strengthextended Kalman filter |
| spellingShingle | Wei Yang Chundi Xiu Jianmin Zhang Dongkai Yang A Novel 3D Pedestrian Navigation Method for a Multiple Sensors-Based Foot-Mounted Inertial System Sensors indoor positioning MEMS-IMU barometer permanent magnet zero-velocity update height estimation magnetic field strength extended Kalman filter |
| title | A Novel 3D Pedestrian Navigation Method for a Multiple Sensors-Based Foot-Mounted Inertial System |
| title_full | A Novel 3D Pedestrian Navigation Method for a Multiple Sensors-Based Foot-Mounted Inertial System |
| title_fullStr | A Novel 3D Pedestrian Navigation Method for a Multiple Sensors-Based Foot-Mounted Inertial System |
| title_full_unstemmed | A Novel 3D Pedestrian Navigation Method for a Multiple Sensors-Based Foot-Mounted Inertial System |
| title_short | A Novel 3D Pedestrian Navigation Method for a Multiple Sensors-Based Foot-Mounted Inertial System |
| title_sort | novel 3d pedestrian navigation method for a multiple sensors based foot mounted inertial system |
| topic | indoor positioning MEMS-IMU barometer permanent magnet zero-velocity update height estimation magnetic field strength extended Kalman filter |
| url | https://www.mdpi.com/1424-8220/17/11/2695 |
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