ITD Modal Decomposition

A data-based decomposition method analogous to Empirical Mode Decomposition (EMD). This technique decomposes complex signals into several proper rotational components and a residual term, enabling better understanding of signal composition and characteristics. Through this decomposition approach, researchers can investigate individual frequency components within signals and their interrelationships, thereby revealing additional information and features underlying the signal. The algorithm implementation generally involves: 1) identifying local extrema points in the signal, 2) constructing envelope functions using interpolation methods like cubic spline, 3) iteratively sifting to extract intrinsic mode functions satisfying certain criteria, and 4) separating residual components. Widely applied in signal processing and data analysis fields, this method facilitates enhanced comprehension and analysis of complex signals and datasets, particularly useful for non-stationary signal analysis where traditional Fourier-based methods may be inadequate.