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Offline Calibration for MEMS Gyroscope G-sensitivity Error Coefficients Based on the Newton Iteration and Least Square Methods

Published online by Cambridge University Press:  11 October 2017

Li Xing ,
Zhi Xiong ,
Jian-ye Liu ,
Wei Luo  and
Ya-zhou Yue
Li Xing
Affiliation:
(Navigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China)
Zhi Xiong*
Affiliation:
(Navigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China)
Jian-ye Liu
Affiliation:
(Navigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China)
Wei Luo
Affiliation:
(Navigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China)
Ya-zhou Yue
Affiliation:
(Flight Automatic Control Research Institute, Xi'an, China)
*
(E-mail: xiongzhi@nuaa.edu.cn)
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Abstract

With the improvement of the bias instability of Micro-Electromechanical Systems (MEMS) gyroscopes, the g-sensitivity error is gradually becoming one of the more important factors that affects the dynamic accuracy of a MEMS gyroscope. Hence there is a need for correcting the g-sensitivity error. However, the traditional calibration of g-sensitivity error uses a centrifuge. The calibration conditions are harsh, the process is complex and the cost is relatively high. In this paper, a fast and simple method of g-sensitivity error calibration for MEMS gyroscopes is proposed. With respect to the bias and random noise of a MEMS gyroscope, the g-sensitivity error magnitude is relatively small and it is simultaneously coupled with the Earth's rotation rate. Therefore, in order to correct the g-sensitivity error, this work models the calibration for g-sensitivity error coefficients, designs an (8+N)-position calibration scheme, and then proposes a fitting method for g-sensitivity error coefficients based on the Newton iteration and least squares methods. Multi-group calibration experiments designed on a MEMS Inertial Measurement Unit (MEMS IMU) product demonstrate that the proposed method can calibrate g-sensitivity error coefficients and correct the g-sensitivity error effectively and simply.

Keywords

G-sensitivity errorLeast square methodNewton iteration methodOffline calibration
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 2 , March 2018 , pp. 352 - 370
Copyright
Copyright © The Royal Institute of Navigation 2017 

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References

REFERENCES

Aggarwal, P., Syed, Z., Niu, X. and El-Sheimy, N. (2008). A Standard Testing and Calibration Procedure for Low Cost MEMS Inertial Sensors and Units. Journal of Navigation, 61(1), 323–336.CrossRefGoogle Scholar
Bancroft, J.B. and Lachapelle, G. (2012) Estimating MEMS Gyroscope g-sensitivity Errors in Foot Mounted Navigation. Proceedings of the Ubiquitous Positioning, Indoor Navigation and Location Based Service (UPINLBS), Helsinki, Finland.CrossRefGoogle Scholar
Borenstein, J., Ojeda, L. and Kwanmuang, S. (2009). Heuristic Reduction of Gyro Drift for Personnel Tracking Systems. Journal of Navigation, 62(1), 41–58.CrossRefGoogle Scholar
Cao, H., Li, H. and Kou, Z. (2016) Optimization and Experimentation of Dual-Mass MEMS Gyroscope Quadrature Error Correction Methods. Sensors, 16(1), 35–42.CrossRefGoogle ScholarPubMed
El-Rabbany, A. and El-Diasty, M. (2004). An Efficient Neural Network Model for De-noising of MEMS-Based Inertial Data. Journal of Navigation, 57(3), 407–415.Google Scholar
Fan, C., Hu, X., He, X., Luo, B. and Tang, K. (2013) Effects of the Micro-gyroscope of G-sensitivity Error on Accuracy of Integrated Navigation. Navigation and Control, 12(4), 1–5.Google Scholar
Fan, C., Hu, X., He, X., Tang, K. and Luo, B. (2014) Observability Analysis of a MEMS INS/GPS Integration System with Gyroscope G-sensitivity Errors. Sensors. 14(9), 16003–16016.Google Scholar
Groves, P.D. (2013). Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems (Second Edition). USA: Artech House, 55–64.Google Scholar
Iozan, L., Collin, J., Pekkalin, O., Hautamäki, J., Takala, J. and Rusu, C. (2010) Measuring the Earth's Rotation Rate Using a Low-Cost MEMS Gyroscope. Proceedings of Symposium Gyro Technology, Karlsruhe, Germany.Google Scholar
Liu, W. (2004). Application Study of Low-cost MIMU Based on MEMS. Changsha, China: National University of Defense Technology, 31–33.Google Scholar
Lv, P., Lai, J.Z., Liu, J.Y. and Nie, M.X. (2014b). The compensation effects of gyros' stochastic errors in a rotational inertial navigation system. Journal of Navigation, 67(6), 1069–1088.Google Scholar
Lv, P., Lai, J.Z., Liu, J.Y. and Qin, G.Q. (2014a). Stochastic Error Simulation Method of Fiber Optic Gyros based on Performance Indicators. Journal of the Franklin Institute, 351, 1501–1516.Google Scholar
MuRata. (2015). SCC2130-D08 Data Sheet. http://www.sensorexpert.com.cn/UploadFiles/Others. MuRata Innovator in Electronics. Accessed March 2015.Google Scholar
Park, B.S., Han, K.J. and Lee, S.W. (2015). Analysis of Compensation for a g-sensitivity Scale-factor Error for a MEMS Vibratory Gyroscope. Journal of Micromechanics & Microengineering, 25(11), 1–5.Google Scholar
Peng, H., Xiong, Z., Wang, R., Liu, J.Y., Zhang, C. (2014). A new dynamic calibration method for IMU deterministic errors of the INS on the Hypersonic Cruise Vehicles. Aerospace Science and Technology, 32(1), 121–130.Google Scholar
Perlmutter, M. and Robin, L. (2012). High-performance, Low Cost Inertial MEMS: A Market in Motion! Proceedings of IEEE/ION PLANS, Myrtle Beach.Google Scholar
Sensonor. (2015). STIM300 Inertial Measurement Unit. http://www.sensonor.com/gyro-products/inertial-measurement-units/stim300.aspx. Accessed October 2015.Google Scholar
Trusov, A.A. (2011). Overview of MEMS Gyroscopes: History, Principles of Operations, Types of Measurements. University of California, Irvine.Google Scholar
Wang, S. (2014). On the Measurement Methods for Inertial Instruments and the Error Analysis Based on Precision Centrifuge. Harbin, China: Harbin Institute of Technology, 46–60.Google Scholar
Weinberg, H. (2011). Gyro Mechanical Performance: the Most Important Parameter. America: Analog Devices Inc.Google Scholar
Wu, Z.W., Yao, M.L. and Ma, H.G. (2013). De-noising MEMS inertial sensors for low cost vehicular attitude estimation based on singular spectrum analysis and independent component analysis. Electronics Letters, 49(14), 892–893.Google Scholar
Xing, Li., Hang, Y.J., Xiong, Z., Liu, J.Y. and Wan, Z. (2016). Accurate Attitude Estimation Using ARS under Conditions of Vehicle Movement Based on Disturbance Acceleration Adaptive Estimation and Correction.Sensors. 16(10), 1716–1731.Google Scholar
Yang, Y., El-Sheimy, N., Goodall, C. and Niu, X. (2007). IMU Signal Software Simulator. Proceedings of ION NTM, San Diego, CA.Google Scholar