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

TL;DR

This paper introduces a novel nonlinear function for Fast-ICA in blind source separation, improving accuracy and convergence speed without needing to select different functions based on signal properties.

Contribution

A new nonlinear function 'sin' is proposed for Fast-ICA, simplifying the process and enhancing performance in blind source separation tasks.

Findings

Improved separation accuracy with the new nonlinear function.

Faster convergence of the Fast-ICA algorithm.

Validated through Matlab simulations.

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.