Local maximum synchrosqueezes form scaling-basis chirplet transform

Yating Hou, Liming Wang, Xiuli Luo and Xingcheng Han

PLOS ONE, 2022, vol. 17, issue 11, 1-18

Abstract: 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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0278223

DOI: 10.1371/journal.pone.0278223

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