Application of polyphase filter banks to wideband monitoring tasks

In the last two decades, both Empirical Mode Decomposition (EMD) and Intrinsic TimeScale 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. INDEX TERMS Adaptive filter bank, denoising algorithms, empirical mode decomposition, Fourier transform, Hilbert-Huang transform, internal oscillations, intrinsic timescale decomposition, spectral analysis, wavelet transform.

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“…Filter banks are widely used in seismic monitoring, radio monitoring and hydroacoustic monitoring [10].…”

Section: Synthesis Of Adaptive Filter Banks Using Emd and Itdmentioning

confidence: 99%

“…This means that the second component of the decomposition is less HF than the first. Noteworthy is the fact that the overall dynamics persists as the IF number increases, up to obtaining the resulting residue [10], [11].…”

Section: Synthesis Of Adaptive Filter Banks Using Emd and Itdmentioning

confidence: 99%