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

Alexander Stroeer, John K. Cannizzo, Jordan B. Camp, and Nicolas Gagarin
Phys. Rev. D 79, 124022 – Published 15 June 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 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.

  • Figure
  • Figure
  • Figure
  • Figure
  • Figure
  • Figure
  • Figure
2 More
  • Received 26 March 2009

DOI:https://doi.org/10.1103/PhysRevD.79.124022

©2009 American Physical Society

Authors & Affiliations

Alexander Stroeer*, John K. Cannizzo, and Jordan B. Camp

  • Laboratory for Gravitational Physics, Goddard Space Flight Center, Greenbelt, Maryland 20771, USA

Nicolas Gagarin

  • Starodub, Incorporated, 3504 Littledale Road, Kensington, Maryland, 20895, USA

  • *Also at CRESST, Department of Astronomy, University of Maryland, College Park, MD 20742, USA. Alexander.Stroeer@nasa.gov
  • Also at CRESST, Physics Department, University of Maryland, Baltimore County, Baltimore, MD 21250, USA.

Article Text (Subscription Required)

Click to Expand

References (Subscription Required)

Click to Expand
Issue

Vol. 79, Iss. 12 — 15 June 2009

Reuse & Permissions
Access Options
Author publication services for translation and copyediting assistance advertisement

Authorization Required


×
×

Images

×

Sign up to receive regular email alerts from Physical Review D

Log In

Cancel
×

Search


Article Lookup

Paste a citation or DOI

Enter a citation
×