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

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.