Blind Signal Separation: Theory and FastICA Algorithm Implementation
Blind Signal Separation (BSS) represents a cutting-edge research topic in signal processing with broad applications across wireless communications, medical diagnostics, speech processing, and seismic signal analysis. The FastICA algorithm, based on negentropy maximization, serves as a powerful method for separating mixed signals. This approach operates by processing non-Gaussian signals under independence assumptions to extract hidden source signals from observed mixtures. Key implementation aspects include whitening preprocessing, iterative optimization for independence, and convergence validation techniques.