Online blind source separation method with adaptive step size in both time-invariant and time-varying cases

Abstract

To effectively balance the convergence speed and steady-state error of online blind source separation, this paper develops an online blind source separation method with adaptive step size based on an equivariant adaptive separation via independence (EASI) algorithm. First, we construct a separation indicator from the convergence condition of EASI that can reveal the separation degree of mixed signals. Next, a new forgetting factor suitable for non-stationary cases is designed to reduce the error accumulation of previous data, and the separation indicator can be adaptively updated. To automatically adjust the step size according to separation degree, a nonlinear mapping between the separation indicator and step size is constructed. Finally, numerical and experimental case studies are provided to evaluate the performance of the proposed method, which comparison of results proves to be more effective. The results of numerical case studies show that the step size of the proposed method can be adaptively adjusted in the separation process and the proposed method can effectively balance convergence speed and steady-state error in both time-invariant and time-varying cases. The results of experimental case studies show that the proposed method has higher estimation accuracy.