Gradient optimized blind sources separation algorithm

Abstract

In this paper, a new gradient optimized blind source separation algorithm (GOA) which aims at improving the convergence performance is proposed. This algorithm modifies the gradient of cost function to make the iteration process of separation matrix closer to its change pattern. To be more specific, by adding the difference value between current time gradient and previous time gradient to the adaptive iterations of separation matrix, the proposed algorithm effectively improves convergence rate. Simulation experiment results show that the GOA has faster convergence rate when compared with the traditional momentum EASI algorithm. In the small step size conditions, the advantage of GOA is even more obvious.