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

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

2009-03-26

arxiv.org/abs/0903.4616v1

Phys.Rev.D79:124022,2009

Physics

Data Analysis, Statistics and Probability

submitted to PRD, 10 pages, 9 figures in color

Scientific paper

10.1103/PhysRevD.79.124022

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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