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

Journal Article · Mon Jun 15 00:00:00 EDT 2009 · Physical Review. D, Particles Fields

DOI:https://doi.org/10.1103/PHYSREVD.79.124022· OSTI ID:21300981

Stroeer, Alexander; Cannizzo, John K; Camp, Jordan B; Gagarin, Nicolas ^[1]

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1. Laboratory for Gravitational Physics, Goddard Space Flight Center, Greenbelt, Maryland 20771 (United States)

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 nonstationary signals with high time-frequency resolution but also renders it susceptible to degradation from noise. We show that complementing the Hilbert-Huang transform 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 ratios.

• OSTI ID: 21300981
• Journal Information: Physical Review. D, Particles Fields, Journal Name: Physical Review. D, Particles Fields Journal Issue: 12 Vol. 79; ISSN PRVDAQ; ISSN 0556-2821
• Country of Publication: United States
• Language: English

Related Subjects

71 CLASSICAL AND QUANTUM MECHANICS
GENERAL PHYSICS
DECOMPOSITION
DENSITY
FOURIER ANALYSIS
HILBERT TRANSFORMATION
KERNELS
NOISE
RESOLUTION
SIGNAL-TO-NOISE RATIO
TIME-SERIES ANALYSIS