Research on MEMS gyroscope motion signal processing method based on stochastic resonance and optimized mode decomposition

Author:

Lu JinboORCID,Ran QiORCID,Wang Hongyan,Tan Kunyu,Pei Zhen,Chen Jinling

Abstract

Abstract In order to process the motion signals of micro electro mechanical system (MEMS) gyroscopes more effectively, this paper proposes a method that combines tri-stable stochastic resonance (TSR) and optimal mode decomposition improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). Firstly, we combined TSR with the crown porcupine optimization (CPO) algorithm and ICEEMDAN to improve the signal-to-noise ratio (SNR) of MEMS gyroscope motion signals. On this basis, the signals are decomposed into many intrinsic mode functions (IMFs). Secondly, the multi-scale permutation entropy (MPE) and dynamic time warping (DTW) are used to form the IMF component judgment criteria, which decompose these IMF components into noise, aliasing, and signal components. Then, Savitzky–Golay (SG) filter and wavelet packet threshold filter are used to filter the noise component and aliasing component separately, and the filtered results are superimposed with the original signal component to obtain the reconstructed signal. Finally, the proposed method is validated through simulation signals and measured motion signals from MEMS gyroscopes, and the results show its effectiveness and practicality.

Funder

the Key Research and Development Programme of Sichuan Province

National Natural Science Foundation of China

Publisher

IOP Publishing

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3