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Improved Morphological Filter Based on Variational Mode Decomposition for MEMS Gyroscope De-Noising.

Yicheng Wu1,2, Chong Shen3,4, Huiliang Cao5,6

  • 1National Key Laboratory of Electronic Measurement Technology, North University of China, Shanxi 030051, China. 1501054235@st.nuc.edu.cn.

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|November 15, 2018
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Summary

This study introduces an adaptive multi-scale method using a combination generalized morphological filter (CGMF) to effectively denoise Micro-Electro-Mechanical Systems (MEMS) gyroscope signals. The novel approach enhances signal quality by targeting noise components and smoothing waveforms.

Keywords:
MEMS gyroscopedenoising algorithmmorphological filtervariational mode decomposition

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Area of Science:

  • Signal Processing
  • Sensor Technology
  • Applied Mathematics

Background:

  • Micro-Electro-Mechanical Systems (MEMS) gyroscopes are crucial for navigation and motion sensing.
  • Output signals from MEMS gyroscopes are susceptible to noise, degrading performance.
  • Conventional denoising methods often struggle with data deviation and optimal parameter selection.

Purpose of the Study:

  • To develop an adaptive multi-scale denoising method for MEMS gyroscope signals.
  • To improve upon existing morphological filter techniques for signal processing.
  • To provide a robust and efficient solution for MEMS gyroscope signal noise reduction.

Main Methods:

  • Employed variational mode decomposition to separate the signal into multi-scale modes.
  • Utilized an adaptive multi-scale combination generalized morphological filter (CGMF).
  • Developed feasible formulae for evaluating denoised signal quality, including power spectral entropy and root mean square error.

Main Results:

  • The adaptive multi-scale CGMF method effectively reduces noise across different signal modes.
  • The method overcomes limitations of conventional morphological filters regarding data deviation.
  • Achieved superior noise suppression and waveform smoothing compared to other signal processing methods.
  • Demonstrated simpler construction and reasonable complexity.

Conclusions:

  • The proposed adaptive multi-scale CGMF method is a feasible and effective algorithm for denoising MEMS gyroscope signals.
  • It offers significant advantages in noise targeting, waveform smoothing, and parameter selection.
  • Experimental results validate the applicability and superior performance of the developed denoising technique.