Variable Step Size Algorithm for Blind Source Separation Using a Combination of Two Adaptive Separation Systems

A critical challenge in adaptive blind source separation is the choice of step size to achieve fast initial convergence speed and low steady state error in time-varying systems. In this paper, we first present an algorithm to restructure the performance index by adopting an auxiliary separation system with some restriction, and then based on a nonlinear updating rule of step-size in the light of the performance index descending with an exponential form, we propose a novel variable step size algorithm for adaptive blind separation. Simulation results show that the convergence and steady-state performance of the proposed method outperforms the fixed step-size and the recently proposed adaptive step-size algorithms in both stationary and non-stationary environments.