Modeling of MEMS gyroscope Random Error Based on Kalman Filter

According to the problem that the serious random error of MEMS gyroscope can affect output accuracy, a Kalman filter algorithm based on time series AR modeling is proposed. Based on the time series modeling requirements, the original data is preprocessed, which include singular point culling, zero mean processing, stationarity test and normality test. And the AIC criterion is used to determine the order of AR model. Then the AR model is established. After that, the Kalman filter algorithm is applied in the MEMS gyroscope output signal. Finally, the result of Kalman algorithm for the gyroscope random error is analyzed through Allan variance. Analysis of Allan variance show that the Kalman filter algorithm can reduce the random error in the output data of the MEMS gyroscope more effectively and improve accuracy of the gyroscope output.