Enhanced Hilbert-Huang Transform Algorithm with Adaptive IMF Screening
Application Background: Empirical Mode Decomposition (EMD) decomposes signals into monocomponent signals called Intrinsic Mode Functions (IMFs), enabling instantaneous frequency calculation through Hilbert transform. The primary challenge in practical Hilbert-Huang transform applications is the endpoint effect. Our solution introduces an adaptive spurious IMF filtering algorithm using residue-to-original-signal correlation coefficient as threshold. Key Technology: Complex signal decomposition into monocomponent signals requires each IMF to satisfy two conditions: (1) Extremum and zero-crossing counts must be equal or differ by one throughout the data length; (2) The mean of upper and lower envelopes must be zero at any point. The implementation involves adaptive sifting with envelope interpolation and statistical boundary handling.