Research on the Modeling of Random Drift Error and Filtering Technology of Low Cost MEMS Gyroscope

Research on the Modeling of Random Drift Error and Filtering Technology of Low Cost MEMS Gyroscope

B. F. Xiong C. TangQ. Wang S. P. Zhu 

College of Engineering and Technology, Southwest University Chongqing, China,

State Grid Chongqing Electric Power Co. Chongqing Electric Power Research Institute, Chongqing, China

Corresponding Author Email:
15 March 2017
15 April 2017
31 March 2017
| Citation



In order to improve the precision of the low cost MEMS gyroscope and reduce the influence of the random drift error on the measurement system. In this paper, the Allan variance method, mean filtering method, time series analysis method and Kalman filtering technique are used to analyze and filter the random error of static output of MEMS gyroscope. The results show that the amplitude of the random drift data is significantly reduced after filtering, the peak value of error data is 19.3% of that before filtering, and the variance is 3.1% of that before filtering. Main noises such as the angle random walk, the bias instability and the rate ramp are effectively suppressed. Above all, the method proposed in this paper can effectively reduce the random drift error of MEMS gyroscope and improve the output precision of MEMS gyroscope.


MEMS gyro, random drift error, kalman filter, mean filter

1. Introduction
2. Allan Variance Principle
3. Test Experiment and Data Processing
4. Mean Filter and Time Series Modeling
5. Kalman Filter
6. Conclusions

The authors wish to thank the Science and Technology Project funded by the State Grid Chongqing Electric Power Co. Chongqing Electric Power Research Institute for their financial support.


1. L.P. Wang, J. Li, J.D. Zhu, Modeling of MEMS gyroscope random error based on allan variance. 2015, Computer Measurement & Control, vol. 23, no. 10, pp. 3488-3491.

2. Y.L. Zhang, H.R. Chu, H.W. Zhang, M.Y. Zhan, Y. Chen, Y.H. Li, Characterists and compensation method of MEMS gyroscope random error, 2008, Chinese Optics, vol. 9, no. 4, pp. 501-510.

3. Y.X. Lv. Random error modeling and compensation for MEMS gyroscope, 2012, Electronic Measurement Technology, Vol. 35, no 12, pp. 41-45.

4. V. Saini, S.C. Rana, M.M. Kube, Online Estimation of State Space Error Model for MEMS IMU.2010, Journal of Modelling & Simulation of Systems, vol. 1, no 4, pp. 219-225.

5. S.H. Du, Combination system and filter algorithm design for MEMS gyroscopes, 2015 Harbin Institute of Technology, Harbin, Master's thesis, pp. 72.

6. Y.F. Ren, K.E. Xi-Zhen, MEMS error modeling based on Allan variance for particle filtering. 2009, Journal of China University of Metrology, vol. 20, no. 2, pp. 102-106.

7. J. Li, J. Liu, W.D. Zhang, W. Yang, Research on Random Error Compensating Methods for MEMS Gyroscope, 2009, Journal of North University of China, Vol. 30, no. 4, pp.381-385.

8. E. Kesavan, N. Gowthaman, S. Tharani, S. Manoharan and E. Arunkumar, Design and implementation of internal model control and particle swarm optimization based PID for heat exchanger system, 2016, International Journal of Heat and Technology, vol. 34, no. 3, pp. 386-390.

9. B.K. Kumar, G.S.N. Raju, Genetic algorithm for the design of phase distribution to reduce quantization lobes, 2014, AMSE JOURNALS-2014-Series: Modelling A, Vol. 87, no. 3, pp. 30-43.

10. Y. Cheng, The research of MEMS inertial devices error analysis and compensation method, 2015, Shenyang Ligong University, Shenyang. Master's thesis, pp. 85.

11. N. Ahmed, K. Sarma, H. Deka, Numerical simulation and modeling of unsteady flow around an airfoil, 2013, International Journal of Heat and Technology, vol. 33, no. 1, pp. 103-108.

12. A. Guergazi, A. Moussi, A. Debilou (Algeria)Application of EKF Algorithm for rotor speed, flux and resistance estimation in induction motors, 2007, AMSE JOURNALS-2007-Series: Modelling A, Vol. 80, no. 1, pp. 28-37.

13. D.S. Chen, Z.H. Shao, X.S. Lei, T.M. Wang, Multiscale fyzzy-adaptive Kalman filtering methods for MEMS gyros random drift, 2009, Journal of Beijing University of Aeronautics and Astronautics, vol. 35, no 2, pp. 246-250.

14. M.H. Shojaeefardi, M. Sh. Mazidi, H. Shojaeefard, M. Mazidi, Air flow velocity prediction by inverting a hot-wire anemometer neural net molel, 2011, International Journal of Heat and Technology, vol. 29, no. 1, pp. 129-134.

15. K. Badshah, Y. Qin, Tightly Coupled Integration of a Low Cost MEMS-INS/GPS System using Adaptive Kalman Filtering, 2016, International Journal of Control & Automation, vol. 9, no. 2, pp. 179-190.

16. S. Meenatchisundaram,S.M. Kulkami,P.R. Venkateswaran,G. Uma,M. Umapathy, Simulation and optimisation of microresonators using sugar and multi-objective genetic algorithm (MOGA), 2009, AMSE JOURNALS-2009-Series: Modelling A, Vol. 82, no. 3, pp. 32-47.

17. H.F. Cao, H.B. Lv, Q.G. Sun, Analyses in random error based on MEMS gyroscope, 2016, Computer Measurement & Control, vol. 39, no. 3, pp. 178-181.

18. C. Chen, W.H. Zhao, H.X. Xu, F.F. Zhou, P. An, Compensation of MEMS gyroscope error based on calman filter, 2013, Journal of Mechanical & Electrical Engineering, vol. 30, no 3, pp. 311-313.

19. S.N. Deepa, G. SugumaranA, modified particle swarm optimization approach for model formulation of linear time invariant discrete systems, 2011, AMSE JOURNALS-2011-Series: Modelling A, Vol. 84, no. 2, pp.1-20.

20. L.Y. Wang, K.P. Zhai, W.T. He, C.Y. Ma, Real-time filtering method for low cost MEMS gyroscope, 2015, Application of Electronic Technology, vol. 41, no 1, pp. 50-52.

21. J.L. Song, X.Z. Wu, L. Guo, Research on modeling and filter of micro electro mechanical system gyroscope random drift, 2012, Missiles and Space Vehicles, vol. 4, pp.  35-38.

22. S. Padmanabhan, M. Sudhakaran, Jeevananthan, An adaptive selective current harmonic elimination technique using recursive least square (RLS) algorithm for three phase AC voltage controllers, 2013, AMSE JOURNALS-2013-Series: Modelling A, Vol. 86, no. 2, pp.71-86.

23. D.M. Zhang, B. Ren, Research on MEMS gyroscope random drift filtering, 2010, Journal of Shenyang Ligong University, vol. 29, no. 2, pp. 82-85.

24. X.L. Wang, N. Li, Error modeling and analysis for random drift of MEMS gyroscopes, 2012, Journal of Beijing University of Aeronautics and Astronautics, vol. 38, no. 2, pp. 170-174.