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arXiv · 0903.4616

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

Alexander Stroeer·John K. Cannizzo·Jordan B. Camp·Nicolas Gagarin
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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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Alexander Stroeer, John K. Cannizzo, Jordan B. Camp, Nicolas Gagarin. 2009-03-26. Methods for detection and characterization of signals in noisy data with the Hilbert-Huang Transform. https://doi.org/10.1103/physrevd.79.124022

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