Local maximum synchrosqueezes form scaling-basis chirplet transform

In recent years, time-frequency analysis (TFA) methods have received widespread attention and undergone rapid development. However, traditional TFA methods cannot achieve the desired effect when dealing with nonstationary signals. Therefore, this study proposes a new TFA method called the local maxi...

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Main Authors: Yating Hou, Liming Wang, Xiuli Luo, Xingcheng Han
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2022-01-01
Series:PLoS ONE
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9707797/?tool=EBI
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author Yating Hou
Liming Wang
Xiuli Luo
Xingcheng Han
author_facet Yating Hou
Liming Wang
Xiuli Luo
Xingcheng Han
author_sort Yating Hou
collection DOAJ
description In recent years, time-frequency analysis (TFA) methods have received widespread attention and undergone rapid development. However, traditional TFA methods cannot achieve the desired effect when dealing with nonstationary signals. Therefore, this study proposes a new TFA method called the local maximum synchrosqueezing scaling-basis chirplet transform (LMSBCT), which is a further improvement of the scaling-basis chirplet transform (SBCT) with energy rearrangement in frequency and can be viewed as a good combination of SBCT and local maximum synchrosqueezing transform. A better concentration in terms of the time-frequency energy and a more accurate instantaneous frequency trajectory can be achieved using LMSBCT. The time-frequency distribution of strong frequency-modulated signals and multicomponent signals can be handled well, even for signals with close signal frequencies and low signal-to-noise ratios. Numerical simulations and real experiments were conducted to prove the superiority of the proposed method over traditional methods.
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spelling doaj.art-79847fb22fa247d086f9069c7fbcd19b2022-12-22T04:36:16ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011711Local maximum synchrosqueezes form scaling-basis chirplet transformYating HouLiming WangXiuli LuoXingcheng HanIn recent years, time-frequency analysis (TFA) methods have received widespread attention and undergone rapid development. However, traditional TFA methods cannot achieve the desired effect when dealing with nonstationary signals. Therefore, this study proposes a new TFA method called the local maximum synchrosqueezing scaling-basis chirplet transform (LMSBCT), which is a further improvement of the scaling-basis chirplet transform (SBCT) with energy rearrangement in frequency and can be viewed as a good combination of SBCT and local maximum synchrosqueezing transform. A better concentration in terms of the time-frequency energy and a more accurate instantaneous frequency trajectory can be achieved using LMSBCT. The time-frequency distribution of strong frequency-modulated signals and multicomponent signals can be handled well, even for signals with close signal frequencies and low signal-to-noise ratios. Numerical simulations and real experiments were conducted to prove the superiority of the proposed method over traditional methods.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9707797/?tool=EBI
spellingShingle Yating Hou
Liming Wang
Xiuli Luo
Xingcheng Han
Local maximum synchrosqueezes form scaling-basis chirplet transform
PLoS ONE
title Local maximum synchrosqueezes form scaling-basis chirplet transform
title_full Local maximum synchrosqueezes form scaling-basis chirplet transform
title_fullStr Local maximum synchrosqueezes form scaling-basis chirplet transform
title_full_unstemmed Local maximum synchrosqueezes form scaling-basis chirplet transform
title_short Local maximum synchrosqueezes form scaling-basis chirplet transform
title_sort local maximum synchrosqueezes form scaling basis chirplet transform
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9707797/?tool=EBI
work_keys_str_mv AT yatinghou localmaximumsynchrosqueezesformscalingbasischirplettransform
AT limingwang localmaximumsynchrosqueezesformscalingbasischirplettransform
AT xiuliluo localmaximumsynchrosqueezesformscalingbasischirplettransform
AT xingchenghan localmaximumsynchrosqueezesformscalingbasischirplettransform