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

Document #:

LIGO-P0900041-v1

Document type:

P - Publications

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.

Files in Document:

• hhtatlowsnr_stroeeretal2009.pdf (669.2 kB)

Topics:

• Data analysis software

Authors:

• Alexander Stroeer

Keywords:

Hilbert Huang Transform HHT data analysis timeseries analysis binary black hole merger bursts

Journal References:

Published in Phys Rev D vol. 79 pg. 124022.