Signal Hilbert-Huang Transform with Code Implementation

This article explores fundamental concepts in signal processing, focusing on the Hilbert-Huang Transform (HHT) and its practical applications. We will delve into Empirical Mode Decomposition (EMD), a key algorithm that decomposes signals into Intrinsic Mode Functions (IMFs) through an iterative sifting process. The implementation typically involves identifying local extrema, interpolating upper/lower envelopes, and extracting oscillatory components meeting IMF criteria. Following decomposition, we demonstrate Hilbert transform processing for each IMF component to obtain instantaneous frequency and amplitude data. The technical discussion covers visualization techniques including 2D and 3D time-frequency distributions using plotting functions like plot3 or surf in MATLAB, time-energy diagrams (instantaneous energy spectrum) calculated as the squared amplitude of Hilbert-transformed IMFs, and frequency-energy diagrams (Hilbert spectrum) representing energy distribution over frequency bands. These methodologies provide deeper insights into non-stationary signal analysis, enabling practical implementation in real-world engineering applications.