MATLAB Implementation of Blind Source Signal Separation

This MATLAB program implements blind source signal separation using two powerful algorithms: JADE (Joint Approximation Diagonalization of Eigenmatrices) and SHIBBS. The implementation includes efficient matrix operations for eigenvalue decomposition and independent component analysis (ICA) to separate mixed signals into their original sources. Both algorithms demonstrate remarkable separation performance through sophisticated statistical analysis of signal characteristics. The program features core functions for signal preprocessing, covariance matrix calculation, and iterative separation processes. JADE algorithm utilizes higher-order statistics to achieve separation by diagonalizing cumulant matrices, while SHIBBS employs a different statistical approach for robust signal extraction. The code structure allows users to easily modify parameters and adapt the algorithms for specific signal types. With excellent scalability and flexibility, users can customize the separation criteria and adjust algorithm parameters according to their specific requirements. The program includes comprehensive signal visualization tools and performance evaluation metrics to analyze separation quality. This MATLAB implementation serves as a valuable tool for researchers and engineers working with mixed signal analysis, providing effective separation results and facilitating deeper understanding of individual signal components through clear, well-documented code structure.