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Direction of Arrival Estimation for Two Sinusoidal FM Signals via Wigner Time-Frequency Distribution in Non-stationary Signal Processing - Implementation with Algorithm and Code Explanations
High-quality research paper with complete source code implementation. The Hilbert-Huang Transform (HHT) represents an advanced signal processing approach for non-stationary signals, comprising two key algorithmic components: Empirical Mode Decomposition (EMD) and Hilbert Spectral Analysis. The EMD algorithm recursively decomposes arbitrary non-stationary signals into Intrinsic Mode Functions (IMFs) representing different characteristic scales. Each IMF undergoes Hilbert transform analysis to extract instantaneous frequency characteristics, with combined spectral results generating comprehensive time-frequency representations. This method effectively stabilizes non-stationary signals by progressively separating intrinsic fluctuations and trends through algorithmic sifting processes.
The Hilbert-Huang Transform (HHT) represents an innovative approach for analyzing non-stationary signals, combining Empirical Mode Decomposition (EMD) with Hilbert spectral analysis. In implementation, signals undergo EMD processing to decompose them into Intrinsic Mode Functions (IMFs) with distinct characteristic scales. Each IMF component then undergoes Hilbert spectral analysis to compute instantaneous frequency and energy distributions. The complete Hilbert spectrum reconstructed from all IMF components provides a time-frequency-energy representation of the original signal, effectively stabilizing non-stationary signals through multi-scale decomposition of fluctuations and trends.