Adaptive step-size EASI algorithm based on separating degree

This paper focuses on the problem of adaptive blind source separation. After detailed analyzing relevant equivariant adaptive source separation via independence (EASI) algorithms, the paper presents a new adaptive step-size EASI algorithm using separating state as the controlling factor for the first time. Because the variability of the new algorithm´s step-size is based on separating state, its learning ratio is chosen adaptively according to separating degree, therefore it can improve convergence speed and reduce the misadjustment error in the steady state simultaneously. Computer simulations confirm the theoretical analysis and show the algorithm performance is superior to other EASI algorithms.