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: 
tangchao_1981@163.com
Page: 
16-31
|
DOI: 
https://doi.org/10.18280/mmc_a.900102
Received: 
15 March 2017
|
Accepted: 
15 April 2017
|
Published: 
31 March 2017
| Citation

OPEN ACCESS

Abstract: 

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.

Keywords: 

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
Acknowledgments

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.

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