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

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.

submitted to PRD, 10 pages, 9 figures in color

References in corpus (2)

• Binary black hole merger dynamics and waveforms
• Application of the Hilbert-Huang Transform to the Search for Gravitational Waves

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