【摘要】 针对MEMS陀螺仪随机漂移产生的误差，提出一种引入Hurst指数的自适应噪声完备集成经验模态分解(CEEMDAN)与自适应卡尔曼滤波(AKF)相结合的去噪模型。首先，通过CEEMDAN对陀螺仪原始信号进行分解，得到一系列频率由高到低的本征模态函数(IMF)和一个残差余量；然后，提出Hurst指数模态筛选机制，将IMF分量划分为噪声IMF、混合IMF和信息IMF;最后，使用自适应卡尔曼滤波器对混合模态分量进行滤波并重构信号。结果表明，CEEMDAN较EMD和EEMD具有更高的分解精度；使用AKF处理混合模态，通过Hurst指数筛选机制重构信号的信噪比相较于排列熵和相关系数法分别提升约12%、36%;使用Hurst指数筛选机制，AKF处理混合模态后重构信号的RMSE较小波阈值滤波降低约23%。更多还原

【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.更多还原