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

TL;DR

This paper presents an enhanced method combining the Hilbert-Huang Transform with filtering and analysis techniques to improve detection and characterization of signals in noisy, non-stationary data.

Contribution

It introduces a novel approach that integrates the HHT with filtering and analysis methods to effectively analyze noisy, non-stationary signals.

Findings

Improved detection of low SNR signals.

Enhanced time-frequency resolution in noisy data.

Effective characterization of non-stationary signals.

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.