Research on signal de-noising technique for MEMS gyro

Abstract

To effectively wipe out random drift and extract valid signal of MEMS gyro, the methods of adaptive Kalman filtering and wavelet analysis are investigated. For the first method, the autoregressive moving average (ARMA) model of random drift is established, which is essential to the adaptive Kalman filter. For the second one, the wavelet basis, decomposition level, and threshold-choosing principle are determined. Then the de-noising test is implemented by using real signal of MEMS gyro, and both methods are of good effectiveness. The contrast analysis between both methods indicates that the adaptive Kalman filtering approach is more suitable for the real-time de-noising of MEMS gyro signal.