Temperature Drift Compensation for Four-Mass Vibration MEMS Gyroscope Based on EMD and Hybrid Filtering Fusion Method

Abstract

This paper presents an improved empirical modal decomposition (EMD) method to eliminate the influence of the external environment, accurately compensate for the temperature drift of MEMS gyroscopes, and improve their accuracy. This new fusion algorithm combines empirical mode decomposition (EMD), a radial basis function neural network (RBF NN), a genetic algorithm (GA), and a Kalman filter (KF). First, the working principle of a newly designed four-mass vibration MEMS gyroscope (FMVMG) structure is given. The specific dimensions of the FMVMG are also given through calculation. Second, finite element analysis is carried out. The simulation results show that the FMVMG has two working modes: a driving mode and a sensing mode. The resonant frequency of the driving mode is 30,740 Hz, and the resonant frequency of the sensing mode is 30,886 Hz. The frequency separation between the two modes is 146 Hz. Moreover, a temperature experiment is performed to record the output value of the FMVMG, and the proposed fusion algorithm is used to analyse and optimise the output value of the FMVMG. The processing results show that the EMD-based RBF NN+GA+KF fusion algorithm can compensate for the temperature drift of the FMVMG effectively. The final result indicates that the random walk is reduced from 99.608°/h/Hz1/2 to 0.967814°/h/Hz1/2, and the bias stability is decreased from 34.66°/h to 3.589°/h. This result shows that the algorithm has strong adaptability to temperature changes, and its performance is significantly better than that of an RBF NN and EMD in compensating for the FMVMG temperature drift and eliminating the effect of temperature changes.

A Baseline Drift-Elimination Algorithm for Strain Measurement-System Signals Based on the Transformer Model

“…Empirical mode decomposition (EMD), which offers a higher degree of adaptability compared to wavelet variation, has also been shown to be effective in removing power line interference and baseline wander noise from electrocardiogram signals [12]. An EMD-based fusion algorithm has been demonstrated to be effective in eliminating the effects of external environments and accurately compensating for temperature drift in MEMS gyroscopes [13]. However, such methods do not perform well in the face of highly randomized drift [14,15] and are difficult to apply in real-time systems [16].…”

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

“…The EMD filtering method is to perform EMD on the noised spectrum to obtain the intrinsic mode function (IMF) of each order. 10 Then, the high-frequency 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.…”

Design and Fabrication of Micro/Nano Sensors and Actuators, Volume II

“…Microelectromechanical system (MEMS) sensors are a miniaturized sensor technology that integrates sensors with microelectronic components using microelectromechanical system manufacturing technology [ 1 , 2 ]. MEMS sensors typically consist of micromechanical structures, sensing circuits, and signal processing circuits, which can be used to measure and detect various physical quantities, such as pressure [ 3 , 4 ], temperature [ 5 , 6 ], acceleration [ 7 , 8 ], angular velocity [ 9 , 10 ], and humidity [ 11 , 12 ]. These micromechanical structures are typically composed of microsprings [ 13 ], thin films [ 14 ], and cantilever beams [ 15 ], and their dimensions typically range from micrometers to millimeters.…”