MATLAB Implementation of Blind Source Separation Algorithm

The Blind Source Separation algorithm enables the extraction of original source signals from mixed observations. This implementation utilizes MATLAB to demonstrate the algorithm's effectiveness through practical signal processing scenarios. The code includes functions for generating synthetic sinusoidal signals and random noise, creating linear mixtures using a mixing matrix, and applying separation techniques such as Independent Component Analysis (ICA) or Principal Component Analysis (PCA). In our experimental validation, the MATLAB program successfully separates pure sinusoidal waveforms and noise components from their combined signals. The implementation features automated testing protocols that verify separation quality through metrics like signal-to-noise ratio (SNR) and correlation analysis. Key functions include signal preprocessing (centering and whitening), iterative separation algorithms, and result visualization through time-domain plots and spectral analysis. This algorithm provides significant advantages for processing complex mixed signals, offering new methodologies and tools for signal processing research and applications. The MATLAB code structure ensures modularity, with separate functions for signal generation, mixing simulation, separation core algorithms, and performance validation, making it suitable for both educational and research purposes.