[0903.4616] Methods for detection and characterization of signals in noisy data with the Hilbert-Huang Transform

Authors: Alexander Stroeer, John K. Cannizzo, Jordan B. Camp, Nicolas Gagarin

Date: 26 Mar 2009

Abstract: The Hilbert-Huang Transform is a novel, adaptive approach to time series analysis that does not make assumptions about the data form. Its adaptive, local character allows the decomposition of non-stationary signals with hightime-frequency resolution but also renders it susceptible to degradation from noise. We show that complementing the HHT with techniques such as zero-phase filtering, kernel density estimation and Fourier analysis allows it to be used effectively to detect and characterize signals with low signal to noise ratio.

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Mar 30, 2009

0903.4616 (/preprints)
2009-03-30, 12:51 [edit]

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