A Noise Reduction Method for Four-Mass Vibration MEMS Gyroscope Based on ILMD and PTTFPF

Abstract

In this paper, the structure and working principle of four-mass vibration MEMS gyroscope (FMVMG) are introduced, and the working modes of FMVMG are simulated and analyzed. On the basis of this, an improved noise reduction method based on interval local mean decomposition (ILMD) and parabolic tracking time-frequency peak filtering (PTTFPF) is proposed. PTTFPF can resample the signal along a parabolic path and select the optimal filtering trajectory, but there is still a contradiction, choosing a short window length may lead to good signal amplitude retention, but the random noise reduction effect is not good, while choosing a long window length may lead to serious amplitude attenuation, but the random noise reduction effect is better. In order to achieve a better balance between effective signal amplitude preservation and random noise reduction, the ILMD method was used to improve PTTFPF. First, the original signal was decomposed into product functions (PFs) by local mean decomposition (LMD) method, and the sample entropy (SE) of each PF was calculated. The PFs are divided into three different components. Then, short window PTTFPF is used for useful PF and long window PTTFPF is used for mixed PF, noise PF is directly removed. Then the final signal is reconstructed. Finally, the denoised useful PF and mixed PF are reconstructed to obtain the final signal. The proposed ILMD-PTTFPF algorithm was verified by temperature experiments. The results show that the denoising performance of the ILMD-PTTFPF algorithm is better than that of traditional wavelet threshold denoising and Kalman filtering.

A Bias Drift Suppression Method Based on ICELMD and ARMA-KF for MEMS Gyros

“…From the results above, we find the combined ICELMD and ARMA-KF method has better denoising ability compared with LMD and CEEMD. Further, the method performs better than [ 25 , 28 ] on standard deviation and variance suppression.…”

“…Compared with LMD and CEEMD, the angle random walk decreased from 0.1585 / and 0.6262 / to 0.0487 / , and the zero bias instability lowered from 0.1642 / h and 0.3147 / h to 0.0509 / h . Moreover, compared with [ 25 , 28 ], the standard deviation suppression ratio improves from 57.87%, 67.22% to 86.82%, and the variance suppression ratio increases from 82.25%, 89.26% to 98.29%, respectively.…”

“…Compared with EMD, LMD can reflect all useful characteristics of the original signal, and the decomposition result is more accurate [ 24 ]. Li applied LMD and parabolic tracking time-frequency peak filtering (PTTFPF) to reduce noise [ 25 ]. Over all, the EMD or LMD were applied for denoising the signal of MEMS gyro, resulting in the decomposed components to be classified.…”

The application of generalized S-transform in the denoising of surface plasmon resonance (SPR) spectrum

“…where q(i) is the polar angles of the spiral equation; a is used to determine the angle between the center point and the search point; R is used to determine the search cycle with the ranges of [5,10] and [0.5,2], respectively; rand is a random number within [0,1]; x(i) and y(i) represent the position of the bald eagle in polar coordinates, the value range is [−1,1]; P i+1 is the location of the next bald eagle.…”

“…The EMD ltering method is to perform EMD on the noised spectrum to obtain the intrinsic mode function (IMF) of each order. 10 Then, the highfrequency IMF components are processed using the threshold method, and the processed high-frequency and low-frequency IMF components are superimposed to obtain the reconstructed signal, which is the denoised signal. This method can effectively remove noises and preserve the detailed information of the spectrum.…”