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.
© M. Vallisneri 2012 — last modified on 2010/01/29
Tantum in modicis, quantum in maximis