research-article

Fourier–Bessel series expansion based empirical wavelet transform for analysis of non-stationary signals

Authors:

Abhijit Bhattacharyya,

Ram Bilas PachoriAuthors Info & Claims

Volume 78, Issue C

Pages 185 - 196

https://doi.org/10.1016/j.dsp.2018.02.020

Published: 01 July 2018 Publication History

Abstract

In this paper, a new method has been presented for the time–frequency (TF) representation of non-stationary signals. The existing empirical wavelet transform (EWT) has been enhanced using Fourier–Bessel series expansion (FBSE) in order to obtain improved TF representation of non-stationary signals. We have used the FBSE method for the spectral representation of the analyzed multi-component signals with good frequency resolution. The scale-space based boundary detection method has been applied for the accurate estimation of boundary frequencies in the FBSE based spectrum of the signal. After that, wavelet based filter banks have been generated in order to decompose non-stationary multi-component signals into narrow-band components. Finally, the normalized Hilbert transform has been applied for the estimation of amplitude envelope and instantaneous frequency functions from the narrow-band components and obtained the TF representation of the analyzed non-stationary signal. We have applied our proposed method for the TF representation of multi-component synthetic signals and real electroencephalogram (EEG) signals. The proposed method has provided better TF representation as compared to existing EWT method and Hilbert–Huang transform (HHT) method, especially when analyzed signal possesses closed frequency components and of short time duration.

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Published In

cover image Digital Signal Processing

Digital Signal Processing Volume 78, Issue C

Jul 2018

404 pages

ISSN:1051-2004

Issue’s Table of Contents

Elsevier Inc.

Publisher

Academic Press, Inc.

United States

Publication History

Published: 01 July 2018

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Zhao ZLi G(2023)Synchrosqueezing-Based Short-Time Fractional Fourier TransformIEEE Transactions on Signal Processing10.1109/TSP.2023.324409771(279-294)Online publication date: 1-Jan-2023
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