Abstract:Aiming at the errors caused by random drift of micro-electro-mechanical system(MEMS) gyroscope, we propose a complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) denoising model with Hurst exponent. Firstly, we decompose the original signal of gyroscope by CEEMDAN to obtain a series of intrinsic mode function (IMF) with high to low frequencies and a residual margin. Secondly, we introduce the Hurst exponential modal screening mechanism, and IMF components are divided into noise IMF, mixed IMF and information IMF. Finally, we filter the mixed modal components by the adaptive Kalman filter and reconstruct the signals. The results show that CEEMDAN has higher decomposition accuracy than EMD and EEMD.Using AKF to deal with mixed mode, the signal-to-noise of reconstructed signals through the Hurst exponential screening mechanism increases by about 12% and 36% compared with permutation entropy and correlation coefficient method. Using Hurst exponential screening mechanism,the RMSE of reconstructed signals of AKF is about 23% lower than that of wavelet threshold filtering.