Noise Reduction for MEMS Gyroscope Signal: A Novel Method Combining ACMP with Adaptive Multiscale SG Filter Based on AMA.

He He,Sun Sun,Wang Wang

doi:10.3390/s19204382

He He, Sun Sun + Show 1 more

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https://doi.org/10.3390/s19204382

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Abstract

In this paper, a novel hybrid method combining adaptive chirp mode pursuit (ACMP) with an adaptive multiscale Savitzky–Golay filter (AMSGF) based on adaptive moving average (AMA) is proposed for offline denoising micro-electromechanical system (MEMS) gyroscope signal. The denoising scheme includes preliminary denoising and further denoising. At the preliminary denoising stage, the original gyroscope signal is decomposed into signal modes one by one using ACMP with modified stopping criterion based on mutual information. Useful information is extracted while most noise is discarded in the residue at this stage. Then, AMSGF is proposed to further denoise the signal modes. Sample variance based on AMA is used to adjust the window size of AMSGF adaptively. Practical MEMS gyroscope signal denoising results under different motion conditions show the superior performance of the proposed method over empirical mode decomposition (EMD)-based denoising, discrete wavelet threshold denoising, and variational mode decomposition (VMD)-based denoising. Moreover, AMSGF is proven to gain a better denoising effect than some other common smoothing methods.

Highlights

The micro-electromechanical system (MEMS) gyroscope, featuring compactness, low cost, and low power consumption, is an important device for measuring the angular velocity of a moving object [1]
The denoised signal is obtained as the sum of all processed modes. This reminder of the paper is organized as follows: Section 2 introduces how to extract useful information contained in the original gyroscope signal by adaptive chirp mode pursuit (ACMP) with modified stopping criterion based on mutual information
The proposed method is suitable for denoising off-line non-stationary gyroscope rate signals

Summary

Introduction

The micro-electromechanical system (MEMS) gyroscope, featuring compactness, low cost, and low power consumption, is an important device for measuring the angular velocity of a moving object [1]. The accuracy of the MEMS gyroscope quickly degrades over time because of high-level noise emerging from the gyroscope outputs. Most gyroscope signals disobey superposition and scaling properties, and have a time-varying distribution parameter. Gyroscope signals are usually non-stationary and nonlinear. Many methods for decomposing nonlinear and non-stationary signal, including wavelet transforms (WTs) [3,4,5], empirical mode decomposition (EMD) [6,7,8,9], and variational mode decomposition (VMD) [1,10,11], can be used to denoise gyroscope signals. EMD as a widely used method in denoising gyroscope signals needs neither auxiliary function and prior knowledge; it has undesirable defects, such as noise-sensitive, mode mixing, and false modes.

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