Hostname: page-component-cb9f654ff-p5m67 Total loading time: 0 Render date: 2025-08-21T05:31:35.922Z Has data issue: false hasContentIssue false
  • English
  • Français

The Standing Calibration Method of MEMS Gyro Bias for Autonomous Pedestrian Navigation System

Published online by Cambridge University Press:  19 October 2016

Yanshun Zhang ,
Xu Yang ,
Xiangming Xing ,
Zhanqing Wang  and
Yunqiang Xiong
Yanshun Zhang
Affiliation:
(School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, China)
Xu Yang*
Affiliation:
(School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, China)
Xiangming Xing
Affiliation:
(Beijing Aerospace Control Instrument Research Institute, Beijing 100854, China)
Zhanqing Wang
Affiliation:
(School of Automation, Beijing Institute of Technology, Beijing 100081, China)
Yunqiang Xiong
Affiliation:
(School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, China)
*
(E-mail: yangxu@buaa.edu.cn)
Get access Rights & Permissions [Opens in a new window]

Abstract

In a waist-worn Pedestrian Navigation System (PNS) based on Dead-Reckoning (DR), heading drift caused by Micro-Electro-Mechanical System (MEMS) gyro bias is an essential factor affecting DR accuracy. Considering the characteristics of pedestrian navigation and the poor bias repeatability of MEMS gyros, this paper presents a standing calibration method for MEMS gyro bias. The current gyro biases for each boot can be calibrated accurately in the initial stage before walking. Since the attitude angles calculated by the output data from magnetic sensor and accelerometers do not drift, this paper applies the reverse algorithm of attitude updating to calculate the angular velocities of human motion. Then the gyro biases at each moment can be acquired by subtraction operation between the measured angular velocities from gyros and the calculated angular velocities of human motion. Finally, in order to restrain the random error caused by sensor noise, the calculated biases in the initial stage are smoothed, and consequently the optimal estimate of current gyro biases after each boot can be obtained. Still and dynamic turntable experiments and a walking experiment are performed to compare and analyse the proposed method and the Zero Angular Rate Update (ZARU) method. Experimental results show that the proposed method can also calibrate the gyro bias accurately in the case of body swaying.

Keywords

Pedestrian NavigationMEMSGyro BiasCalibration

Information

Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 3 , May 2017 , pp. 607 - 617
Copyright
Copyright © The Royal Institute of Navigation 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Abdulrahim, K., Hide, C., Moore, T. and Hill, C. (2012). Using constraints for shoe mounted indoor pedestrian navigation. Journal of Navigation, 65(1), 15–28.CrossRefGoogle Scholar
Abdulrahim, K., Hide, C., Moore, T. and Hill, C. (2014). Rotating a mems inertial measurement unit for a foot-mounted pedestrian navigation. Journal of Computer Science, 10(12), 2619.CrossRefGoogle Scholar
Alvarez, J.C., Lopez, A.M., Gonzalez, R.C. and Alvarez, D. (2012). Pedestrian dead reckoning with waist-worn inertial sensors. In Instrumentation and Measurement Technology Conference (I2MTC), 2012 IEEE International, 24–27.CrossRefGoogle Scholar
Bancroft, J.B. and Lachapelle, G. (2012). Use of magnetic quasi static field (QSF) updates for pedestrian navigation. In Position Location and Navigation Symposium (PLANS), 2012 IEEE/ION, 605–612.CrossRefGoogle Scholar
Basiri, A., Amirian, P. and Winstanley, A. (2014). The use of quick response (QR) codes in landmark-based pedestrian navigation. International Journal of Navigation and Observation, 2014(7), 1–7.CrossRefGoogle Scholar
Cho, S.Y. and Park, C.G. (2006). MEMS based pedestrian navigation system. Journal of Navigation, 59(1), 135–153.CrossRefGoogle Scholar
Fang, L., Antsaklis, P.J., Montestruque, L., McMickell, M.B., Lemmon, M. and Sun, Y. (2005). Design of a wireless assisted pedestrian dead reckoning system – the navmote experience. IEEE Transactions on Instrumentation & Measurement, 54(6), 2342–2358.CrossRefGoogle Scholar
Fokin, L.A. and Shchipitsin, A.G. (2008). Strapdown inertial navigation systems for high precision near-Earth navigation and satellite geodesy: Analysis of operation and errors. Journal of Computer and Systems Sciences International, 47(3), 485–497.CrossRefGoogle Scholar
Foxlin, E. (2005). Pedestrian tracking with shoe-mounted inertial sensors. Computer Graphics and Applications, IEEE, 25(6), 38–46.CrossRefGoogle ScholarPubMed
Jiménez, A.R., Seco, F., Zampella, F., Prieto, J.C. and Guevara, J. (2012). Improved heuristic drift elimination with magnetically-aided dominant directions (mihde) for pedestrian navigation in complex buildings. Journal of Location Based Services, 6(3), 186–210.CrossRefGoogle Scholar
Jiménez, A.R., Seco, F., Prieto, J.C. and Guevara, J. (2010, March). Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU. In Positioning Navigation and Communication (WPNC), 2010 7th Workshop on, 135–143.CrossRefGoogle Scholar
Kemp, B., Janssen, A.J.M.W. and Kamp, B.V.D. (1999). Body position can be monitored in 3d using accelerometers and earth-magnetic field sensors. Electroencephalography & Clinical Neurophysiology, 109(6), 484–8.CrossRefGoogle Scholar
Lan, K.C. and Shih, W.Y. (2014). An indoor location tracking system for smart parking. International Journal of Parallel, Emergent and Distributed Systems, 29(3), 215–238.CrossRefGoogle Scholar
Li, J., Fang, J. and Du, M. (2012). Error analysis and gyro-bias calibration of analytic coarse alignment for airborne POS. Instrumentation and Measurement, IEEE Transactions on, 61(11), 3058–3064.Google Scholar
Park, J., Kim, Y. and Lee, J. (2012). Waist mounted pedestrian dead-reckoning system. In Ubiquitous Robots and Ambient Intelligence (URAI), 2012 9th International Conference on, 335–336.Google Scholar
Pinchin, J., Hide, C., Abdulrahim, K., Moore, T. and Hill, C. (2011). Integration of heading-aided MEMS IMU with GPS for pedestrian navigation. In Proceedings of the 24th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2011), 1346–1356.Google Scholar
Townsend, C.P. and Arms, S.W. (2008). Solid state orientation sensor with 360 degree measurement capability. U.S. Patent No. 7,433,798. Washington, DC: U.S. Patent and Trademark Office. Google Scholar
Xie, B., Qin, Y.Y. and Wan, Y.H. (2011). Multiposition calibration method of laser gyro SINS. Journal of Chinese Inertial Technology, 2, 008.Google Scholar
Zhang, R., Bannoura, A., Hoflinger, F., Reindl, L.M. and Schindelhauer, C. (2013). Indoor localization using a smart phone. In Sensors Applications Symposium (SAS), 2013 IEEE, 38–42.Google Scholar