Adaptive Signal Processing Algorithms Based on EMD and ITD

Alexander Voznesensky,Dmitrii Kaplun

doi:10.1109/access.2019.2956077

Alexander Voznesensky, Dmitrii Kaplun

Open Access

https://doi.org/10.1109/access.2019.2956077

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Journal: IEEE access : practical innovations, open solutions	Publication Date: Jan 1, 2019
Citations: 24	License type: CC BY 4.0

Abstract

In the last two decades, both Empirical Mode Decomposition (EMD) and Intrinsic Time-Scale Decomposition (ITD) algorithms deserved a variety of applications in various fields of science and engineering due to their obvious advantages compared to conventional (e.g. correlation- or spectral-based analysis) approaches like the ability of their direct application to non-stationary signal analysis. However, high computational complexity remains a common drawback of these otherwise universal and powerful algorithms. Here we compare similarly designed signal analysis algorithms utilizing either EMD or ITD as their core functions. Based on extensive computer simulations, we show explicitly that the replacement of EMD by ITD in several otherwise similar signal analysis scenarios leads to the increased noise robustness with simultaneous considerable reduction of the processing time. We also demonstrate that the proposed algorithms modifications could be successfully utilized in a series of emerging applications for processing of non-stationary signals.

Highlights

Majority of the observational signals considered in various fields of knowledge are non-stationary indicated by the variability of their statistical characteristics over time
Kaplun: Adaptive Signal Processing Algorithms Based on Empirical Mode Decomposition (EMD) and Intrinsic Time-Scale Decomposition (ITD)
We propose a novel approach to adaptive signal processing based on EMD and ITD

Summary

INTRODUCTION

Majority of the observational signals considered in various fields of knowledge are non-stationary indicated by the variability of their statistical characteristics over time. Kaplun: Adaptive Signal Processing Algorithms Based on EMD and ITD Another drawback is that, due to their smoothness, the fitting harmonic functions often appear ineffective for the analysis of signals containing abrupt and/or stepwise changes. Matching pursuit algorithm (MPA) is another technique for spectral analysis This algorithm allows decomposing the input signal using the following different basis functions: wavelets, sine waves, damped sine waves, polynomials, etc. These functions form the atom dictionary (the set of basic functions) where each function is localized in time and frequency domains. In contrast to harmonic analysis, that setting in advance the signal decomposition basis, empirical modes are calculated during the process, making the algorithm adaptive.

SYNTHESIS OF ADAPTIVE FILTER BANKS USING EMD AND ITD

DENOISING USING EMD AND ITD

CONCLUSION