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Adaptive Signal Processing Algorithms Based on EMD and ITD
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DOI：10.1109/ACCESS.2019.2956077.png)
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摘要
En 中文
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.
Keyword:
Adaptive filter bank
denoising algorithms
empirical mode decomposition
Fourier transform
Hilbert-Huang transform
internal oscillations
intrinsic time-scale decomposition
spectral analysis
wavelet transform
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3.6
论文数:
9.8W
被引数:
29.4W
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