Fixed-point blind source separation algorithm based on ICA

Abstract

This paper introduces the fixed-point learning algorithm based on independent component analysis (ICA); the model and process of this algorithm and simulation results are presented. Kurtosis was adopted as the estimation rule of independence. The results of the experiment show that compared with the traditional ICA algorithm based on random grads, this algorithm has advantages such as fast convergence and no necessity for any dynamic parameter, etc. The algorithm is a highly efficient and reliable method in blind signal separation.