Methods for detection and characterization of signals in noisy data with the Hilbert-Huang Transform
Data Analysis, Statistics and Probability
2010-04-22 v1 Numerical Analysis
General Relativity and Quantum Cosmology
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.
Keywords
Cite
@article{arxiv.0903.4616,
title = {Methods for detection and characterization of signals in noisy data with the Hilbert-Huang Transform},
author = {Alexander Stroeer and John K. Cannizzo and Jordan B. Camp and Nicolas Gagarin},
journal= {arXiv preprint arXiv:0903.4616},
year = {2010}
}
Comments
submitted to PRD, 10 pages, 9 figures in color