ARMA-AKF Model of MEMS Gyro Rotation Data Random Drift Compensation

Wei Peng Su; Yong Sheng Hao; Qi Chang Li

doi:10.4028/www.scientific.net/AMM.321-324.549

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 321-324ARMA-AKF Model of MEMS Gyro Rotation Data Random...

ARMA-AKF Model of MEMS Gyro Rotation Data Random Drift Compensation

Abstract:

Application of MEMS angular rate gyro attitude when monitoring for long-term, zero-point drift and random error in cumulative points after long-term monitoring errors can significantly increase the measurement error. Using ARMA model of MEMS gyros random drift modeling and error compensation method using Adaptive Kalman Filter, which increases gyro attitude measurement of long-term reliability Last experiment. The method in accordance with the principle of time series analysis, integration of observations and estimates of actual system so that it can reflect the influence of external disturbance and noise on the system. It can also reflect the influence of system disturbance on actual system performance improves estimation precision.

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Periodical:

Applied Mechanics and Materials (Volumes 321-324)

Pages:

549-552

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.321-324.549

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Online since:

June 2013

Authors:

Wei Peng Su, Yong Sheng Hao, Qi Chang Li

Keywords:

Adaptive Kalman Filter, ARMA Model, Fitting Residue, Measurement Error

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References

[1] P. J. Brockwell, D. A. Richard. Time Series, Theory and Methods (2ed) [M]. Springer, 2001: 36~45.

Google Scholar

[2] B. P. Mann, F. A. Khasanwneh, R. F.ales. Usinginformation to generate derivative coordinates from noisy time series [J]. Commun Nonlinear Sci Numer Simulat, 2011, 16: 2999-3004.

DOI: 10.1016/j.cnsns.2010.11.011

Google Scholar

[3] S. Cheong, R. R. Bitmead. Instubility detection of ARMA system based on AR system identification [J]. Systems & Control Letters, 2011, 60: 185-191.

DOI: 10.1016/j.sysconle.2010.12.003

Google Scholar

[4] S. B. Horowitz. Development of a MEMS-based acoustic energy harvester [D]. Dissertation for degree of Doctor of Philosophy in University of Florida, 2005.

Google Scholar

[5] G. E. P. Box, G. M. Jenkins, G. C. Reinsel. Time series analysis: Forecasting and control [M]. Beijing: Post＆Telecom Press, 2005.

Google Scholar

[6] S. Cheong, R. R. Bitmead. Instubility detection of ARMA system based on AR system identification [J]. Systems & Control Letters, 2011, 60: 185-191.

DOI: 10.1016/j.sysconle.2010.12.003

Google Scholar