Methods for detection and characterization of signals in noisy data with the Hilbert-Huang Transform
classification
⚛️ physics.data-an
cs.NAgr-qc
keywords
signalsadaptiveallowsanalysisdatahilbert-huangnoisetransform
read the original 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.
This paper has not been read by Pith yet.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.