Blind source separation using Fast-ICA with a novel nonlinear function

2019/07/08 by Pengfei Xu, Xu, Pengfei, Yinjie Jia +3
Engineering · #FOS: Electrical engineering #Signal Processing (eess.SP) #eess.SP #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1907.03432

5 pages, 2 figures

arxiv created 2019/07/08 · arxiv updated 2019/07/09

Abstract

Blind source separation(BSS) is a hotspot in signal processing, and independent component analysis (ICA) is a very effective tool for solving the BSS problem. In order to improve the performance of the separation, a new nonlinear function sin was introduced. It can replace the commonly used classical functions (tanh, gauss and pow3) and does not need to select different nonlinear functions according to the Gauss property of signals. The two Matlab simulation results show that the improved Fast-ICA algorithm with the proposed nonlinearity can not only improve the separation accuracy but also speed up the convergence of blind source separation.

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