Hilbert-Huang Transform (HHT): A Novel Non-Stationary Signal Processing Technique

The Hilbert-Huang Transform (HHT) constitutes a groundbreaking non-stationary signal processing technique that integrates two core components: Empirical Mode Decomposition (EMD) and Hilbert spectral analysis. This methodology proves particularly valuable for handling arbitrary non-stationary signals through algorithmic decomposition into multiple Intrinsic Mode Functions (IMFs) - each representing distinct oscillatory modes with specific characteristic scales. From an implementation perspective, the EMD algorithm employs iterative sifting processes to extract IMFs based on local extrema and envelope characteristics. Following decomposition, each IMF undergoes Hilbert transform processing to calculate analytical signals, from which instantaneous frequency and amplitude can be derived computationally. The aggregation of Hilbert spectra from all IMF components generates a comprehensive time-frequency distribution that accurately characterizes the original signal's frequency content and energy variations. Essentially, HHT performs signal stabilization by hierarchically separating inherent multi-scale fluctuations and trends, ultimately enabling precise signal representation through instantaneous frequency and energy parameters. This approach introduces significant versatility and analytical capabilities to modern signal processing applications.