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Calibration Analysis of High-G MEMS Accelerometer Sensor Based on Wavelet and Wavelet Packet Denoising

High-G accelerometers are mainly used for motion measurement in some special fields, such as projectile penetration and aerospace equipment. This paper mainly explores the wavelet threshold denoising and wavelet packet threshold denoising in wavelet analysis, which is more suitable for high-G piezor...

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Detalles Bibliográficos
Autores principales: Shi, Yunbo, Zhang, Juanjuan, Jiao, Jingjing, Zhao, Rui, Cao, Huiliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7916207/
https://www.ncbi.nlm.nih.gov/pubmed/33572421
http://dx.doi.org/10.3390/s21041231
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author Shi, Yunbo
Zhang, Juanjuan
Jiao, Jingjing
Zhao, Rui
Cao, Huiliang
author_facet Shi, Yunbo
Zhang, Juanjuan
Jiao, Jingjing
Zhao, Rui
Cao, Huiliang
author_sort Shi, Yunbo
collection PubMed
description High-G accelerometers are mainly used for motion measurement in some special fields, such as projectile penetration and aerospace equipment. This paper mainly explores the wavelet threshold denoising and wavelet packet threshold denoising in wavelet analysis, which is more suitable for high-G piezoresistive accelerometers. In this paper, adaptive decomposition and Shannon entropy criterion are used to find the optimal decomposition layer and optimal tree. Both methods use the Stein unbiased likelihood estimation method for soft threshold denoising. Through numerical simulation and Machete hammer test, the wavelet threshold denoising is more suitable for the dynamic calibration of a high-G accelerometer. The wavelet packet threshold denoising is more suitable for the parameter extraction of the oscillation phase.
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spelling pubmed-79162072021-03-01 Calibration Analysis of High-G MEMS Accelerometer Sensor Based on Wavelet and Wavelet Packet Denoising Shi, Yunbo Zhang, Juanjuan Jiao, Jingjing Zhao, Rui Cao, Huiliang Sensors (Basel) Article High-G accelerometers are mainly used for motion measurement in some special fields, such as projectile penetration and aerospace equipment. This paper mainly explores the wavelet threshold denoising and wavelet packet threshold denoising in wavelet analysis, which is more suitable for high-G piezoresistive accelerometers. In this paper, adaptive decomposition and Shannon entropy criterion are used to find the optimal decomposition layer and optimal tree. Both methods use the Stein unbiased likelihood estimation method for soft threshold denoising. Through numerical simulation and Machete hammer test, the wavelet threshold denoising is more suitable for the dynamic calibration of a high-G accelerometer. The wavelet packet threshold denoising is more suitable for the parameter extraction of the oscillation phase. MDPI 2021-02-09 /pmc/articles/PMC7916207/ /pubmed/33572421 http://dx.doi.org/10.3390/s21041231 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shi, Yunbo
Zhang, Juanjuan
Jiao, Jingjing
Zhao, Rui
Cao, Huiliang
Calibration Analysis of High-G MEMS Accelerometer Sensor Based on Wavelet and Wavelet Packet Denoising
title Calibration Analysis of High-G MEMS Accelerometer Sensor Based on Wavelet and Wavelet Packet Denoising
title_full Calibration Analysis of High-G MEMS Accelerometer Sensor Based on Wavelet and Wavelet Packet Denoising
title_fullStr Calibration Analysis of High-G MEMS Accelerometer Sensor Based on Wavelet and Wavelet Packet Denoising
title_full_unstemmed Calibration Analysis of High-G MEMS Accelerometer Sensor Based on Wavelet and Wavelet Packet Denoising
title_short Calibration Analysis of High-G MEMS Accelerometer Sensor Based on Wavelet and Wavelet Packet Denoising
title_sort calibration analysis of high-g mems accelerometer sensor based on wavelet and wavelet packet denoising
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7916207/
https://www.ncbi.nlm.nih.gov/pubmed/33572421
http://dx.doi.org/10.3390/s21041231
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