Multiple synchro-tuning chirplet transform.
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| Title: | Multiple synchro-tuning chirplet transform. |
|---|---|
| Authors: | Xu, Lingji1,2,3 (AUTHOR) xulj26@mail.sysu.edu.cn, Chen, Lixing1,2,3 (AUTHOR) chenlx76@mail2.sysu.edu.cn, Wang, Zixin1,2,3 (AUTHOR) wangzx83@mail2.sysu.edu.cn, Jiang, Weihua1,2,3 (AUTHOR) jiangwh28@mail.sysu.edu.cn, Li, Zhenglin1,2,3 (AUTHOR) lizhlin29@mail.sysu.edu.cn |
| Source: | Digital Signal Processing. Jan2024, Vol. 144, pN.PAG-N.PAG. 1p. |
| Subjects: | Time-frequency analysis, Hilbert-Huang transform, Signals & signaling, Computer simulation |
| Abstract: | Time-frequency analysis (TFA) is an effective tool for characterizing non-stationary signals. Due to the limited time-frequency resolution capability, traditional TFA methods cannot achieve the optimal time-frequency representation (TFR). Based on the post-processing technique, a novel TFA method named multiple synchro-tuning chirplet transform (MSTCT) is proposed to obtain high-resolution and robust TFR for non-stationary signals. The MSTCT first estimates the spectral energy, instantaneous frequency (IF), and chirp rate (CR) of non-stationary signals with high time-frequency resolution, enabling precise energy localization on the time-frequency plane. Then, a multiple reassignment procedure is proposed to concentrate the blurry time-frequency energy and yield a better TFR. Moreover, benefiting from the multiple squeezing mechanism in the frequency axis, the MSTCT holds the potential to reconstruct the signal with high accuracy. Both the simulation results and the real-world experiment using the bat echolocation data demonstrate that the proposed MSTCT algorithm achieves superior performance in the time-frequency resolution, noise immunity, and reconstruction capability compared to the state-of-art linear TFA methods. • A novel TFA method named MSTCT is proposed to obtain high resolution and robust TFR of non-stationary signals. • A robust CR estimator is proposed to concentrate the blurry time-frequency energy under the multiple squeezing mechanism. • The numerical simulation and the real-world signal analysis verify the superior performance of the MSTCT. [ABSTRACT FROM AUTHOR] |
| Copyright of Digital Signal Processing is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multiple synchro-tuning chirplet transform. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xu%2C+Lingji%22">Xu, Lingji</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> xulj26@mail.sysu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Lixing%22">Chen, Lixing</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> chenlx76@mail2.sysu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Zixin%22">Wang, Zixin</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> wangzx83@mail2.sysu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Jiang%2C+Weihua%22">Jiang, Weihua</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> jiangwh28@mail.sysu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Zhenglin%22">Li, Zhenglin</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> lizhlin29@mail.sysu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Digital+Signal+Processing%22">Digital Signal Processing</searchLink>. Jan2024, Vol. 144, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Time-frequency+analysis%22">Time-frequency analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Hilbert-Huang+transform%22">Hilbert-Huang transform</searchLink><br /><searchLink fieldCode="DE" term="%22Signals+%26+signaling%22">Signals & signaling</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Time-frequency analysis (TFA) is an effective tool for characterizing non-stationary signals. Due to the limited time-frequency resolution capability, traditional TFA methods cannot achieve the optimal time-frequency representation (TFR). Based on the post-processing technique, a novel TFA method named multiple synchro-tuning chirplet transform (MSTCT) is proposed to obtain high-resolution and robust TFR for non-stationary signals. The MSTCT first estimates the spectral energy, instantaneous frequency (IF), and chirp rate (CR) of non-stationary signals with high time-frequency resolution, enabling precise energy localization on the time-frequency plane. Then, a multiple reassignment procedure is proposed to concentrate the blurry time-frequency energy and yield a better TFR. Moreover, benefiting from the multiple squeezing mechanism in the frequency axis, the MSTCT holds the potential to reconstruct the signal with high accuracy. Both the simulation results and the real-world experiment using the bat echolocation data demonstrate that the proposed MSTCT algorithm achieves superior performance in the time-frequency resolution, noise immunity, and reconstruction capability compared to the state-of-art linear TFA methods. • A novel TFA method named MSTCT is proposed to obtain high resolution and robust TFR of non-stationary signals. • A robust CR estimator is proposed to concentrate the blurry time-frequency energy under the multiple squeezing mechanism. • The numerical simulation and the real-world signal analysis verify the superior performance of the MSTCT. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Digital Signal Processing is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.dsp.2023.104252 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Time-frequency analysis Type: general – SubjectFull: Hilbert-Huang transform Type: general – SubjectFull: Signals & signaling Type: general – SubjectFull: Computer simulation Type: general Titles: – TitleFull: Multiple synchro-tuning chirplet transform. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xu, Lingji – PersonEntity: Name: NameFull: Chen, Lixing – PersonEntity: Name: NameFull: Wang, Zixin – PersonEntity: Name: NameFull: Jiang, Weihua – PersonEntity: Name: NameFull: Li, Zhenglin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 10512004 Numbering: – Type: volume Value: 144 Titles: – TitleFull: Digital Signal Processing Type: main |
| ResultId | 1 |
