MATLAB Implementation of Natural Gradient Algorithm for Blind Source Separation

We can implement the natural gradient algorithm in MATLAB to simulate the blind source separation process. The implementation involves creating a separation matrix that adaptively updates using the natural gradient descent method, which provides better convergence properties compared to conventional gradient algorithms. During the simulation, we can observe the learning convergence curve by plotting the evolution of the separation performance index (such as Amari distance or signal-to-interference ratio) against iteration count. This curve evaluation allows us to assess algorithm performance under different conditions, including various source distributions and mixing scenarios. Through analysis of the convergence behavior, we can identify optimal learning rates, validate separation accuracy, and make necessary improvements to enhance both the precision and stability of separated signals. The MATLAB code typically includes key functions for whitening preprocessing, gradient computation using Lie group structure, and real-time performance monitoring.