基于CEEMDAN-WP-SG的MEMS陀螺仪去噪算法

MEMS gyroscope denoising algorithm based on CEEMDAN-WP-SG

为了减小MEMS陀螺仪随机误差,提出了一种新的去噪算法。该算法首先通过自适应噪声完备经验模态分解(CEEMDAN)将原始数据分解为多个本征模态函数(IMF),并根据多尺度排列熵与马氏距离将IMF分为噪声IMF、混叠IMF和信号IMF;其次对噪声IMF用小波包(WP)去噪,对混叠IMF用Savitzky-Golay滤波器(SG)去噪;最后,把处理后的IMF和信号IMF进行重构,得到去噪后的信号。通过所提方法对Bumps信号进行实验分析,去噪后信号从6 dB提高至17 dB,均方误差降低71.9%;对实测陀螺仪静态数据进行分析,实验结果证明去噪后信号的角度随机游走降低31.5%,表明该方法能显著提高MEMS陀螺仪的精度。

A new denoising algorithm is proposed aiming to decrease the random error of MEMS gyroscope.Firstly,the original data is decomposed into multiple intrinsic mode functions(IMFs)using complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN).Then the IMFs are divided into noise IMF,mixed IMF,and signal IMF according to multi-scale permutation entropy with Mahalanobis distance.Next,the noise IMF is denoised by wavelet packet(WP)and the mixed IMF is denoised by Savitzky-Golay filter(SG).Finally,the denoised signal is obtained via reconstructing the processed IMF and the signal IMF.The bumps signal is increased from 6 dB to 17 dB,and the mean square error is reduced by 71.9%after denoising through the proposed method.The angular random walk of the denoised signal is reduced by 31.5%in the experimental analysis of the measured gyroscope static data,which illustrates that the proposed method can predominantly improve the accuracy of MEMS gyroscope accuracy.