[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.

abs pdf

Mar 30, 2009

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


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