Analysis of Nonlinear Signals using EMD (Empirical Mode Decomposition) in Hilbert-Huang Transform

The Empirical Mode Decomposition (EMD) method within the Hilbert-Huang Transform provides a highly effective approach for analyzing nonlinear signals. The EMD algorithm operates by decomposing signals based on their local characteristics, iteratively extracting a series of intrinsic mode functions (IMDs) and their corresponding instantaneous frequencies. This decomposition mechanism enables deeper understanding and analysis of nonlinear signal properties. The fixed IMFs capture localized signal information at different time scales, while the instantaneous frequencies characterize temporal frequency variations. Practically, EMD implementation involves key steps like envelope estimation using cubic spline interpolation, local extrema detection, and sifting processes to meet IMF criteria. Consequently, the EMD methodology demonstrates broad applicability potential in signal processing and analytical domains, particularly for non-stationary and nonlinear data analysis.