Blind Source Separation Based on Genetic Algorithm-Optimized Multiuser Kurtosis

Blind source separation is a challenging problem in signal processing, involving the separation of mixed signals into their individual sources. This paper introduces a novel approach based on multiuser kurtosis to address the blind source separation problem. The proposed technique utilizes the kurtosis of the signals to estimate both the source signals and the mixing matrix. By leveraging the higher-order statistical properties of the signals, particularly their fourth-order statistics, the mixing matrix is estimated and used to separate the original sources. To enhance the separation performance, a multiuser extension of the kurtosis-based approach is introduced, enabling simultaneous separation and retrieval of multiple sources. This extension employs a joint diagonalization approach to estimate the mixing matrix and perform source separation. The performance of the proposed model is evaluated on synthetic and real-world datasets, and compared against other state-of-the-art blind source separation techniques. The experimental results demonstrate that the multiuser kurtosis-based algorithm outperforms existing methods in terms of separation accuracy and computational efficiency. Furthermore, the algorithm exhibits robustness to noise and can handle non-linear mixing models. Additionally, a genetic algorithm is employed in this study to further enhance the separation/estimation performance of the multiuser kurtosis-based blind source separation. The potential of the proposed model in speech and image processing applications is demonstrated, showing competitive performance compared to existing techniques. The simulation results confirm the promising nature of the proposed genetic algorithm-optimized multiuser kurtosis-based method for solving blind source separation problems in various signal and image processing applications.