Research on Novel Denoising Method of Variational Mode Decomposition in MEMS Gyroscope-Reference-Cited by-同舟云学术

Research on Novel Denoising Method of Variational Mode Decomposition in MEMS Gyroscope

Published:2021-02-01 Issue:1 Volume:21 Page:19-24
ISSN:1335-8871
Container-title:Measurement Science Review
language:en
Short-container-title:

Author:

Wang Xiaolei¹,Cao Huiliang²,Jiao Yuzhao¹,Lou Taishan¹,Ding Guoqiang¹,Zhao Hongmei¹,Duan Xiaomin³

Affiliation:

1. College of Electrical and Information Engineering , Zhengzhou University of Light Industry , Dongfeng Road, No.5, Zhengzhou 450002, People’s Republic of China

2. National Key Laboratory for Electronic Measurement Technology , School of Instrument and Electronics , North University of China , Xueyuan Road, No.3, Taiyuan 030051, People’s Republic of China

3. School of Electronic Science and Engineering , University of Electronic Science and Technology of China , Chengdu, 611731, People’s Republic of China

Abstract

Abstract The noise signal in the gyroscope is divided into four levels: sampling frequency level, device bandwidth frequency level, resonant frequency level, and carrier frequency level. In this paper, the signal in the dual-mass MEMS gyroscope is analyzed. Based on the variational mode decomposition (VMD) algorithm, a novel dual-mass MEMS gyroscope noise reduction method is proposed. The VMD method with different four-level center frequencies is used to process the original output signal of the MEMS gyroscope, and the results are analyzed by the Allan analysis of variance, which shows that the ARW of the gyroscope is increased from 1.998*10−1°/√h to 1.552*10−4°/√h, BS increased from 2.5261°/h to 0.0093°/h.

Publisher

Walter de Gruyter GmbH

Subject

Instrumentation,Biomedical Engineering,Control and Systems Engineering

Link

https://www.sciendo.com/pdf/10.2478/msr-2021-0003

Reference26 articles.

1. [1] Shen, C., Liu, X., Cao, H., Zhou, Y., Liu, J., Tang, J., Guo, X., Huang, H., Chen, X. (2019). Brain-like navigation scheme based on MEMS-INS and place recognition. Applied Sciences, 9 (8), 1708.

2. [2] Zhang, Y., Liu, W., Yang, X., Xing, S. (2015). Hidden Markov model-based pedestrian navigation system using MEMS inertial sensors. Measurement Science Review, 15 (1), 35-43.

3. [3] Cao, H., Cui, R., Liu, W., et al. (2021). Dual mass MEMS gyroscope temperature drift compensation based on TFPF-MEA-BP algorithm. Sensor Review, DOI: 10.1108/SR-09-2020-0205. (in press)

4. [4] Guo, X., Tang, J., Li, J., Shen, C., Liu, J. (2019). Attitude measurement based on imaging ray tracking model and orthographic projection with iteration algorithm. ISA Transactions, 95, 379-391.

5. [5] Mellal, I., Laghrouche, M., Bui, H. (2017). Field programmable gate array (FPGA) respiratory monitoring system using a flow microsensor and an accelerometer. Measurement Science Review, 17 (2), 61-67.

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