Blind Sequential Extraction of Post-Nonlinearly Mixed Sources using Kalman Filtering
Wai Yie Leong, Danilo P. Mandic · 2006
A novel approach which extends blind source separation (BSS) of one or group of sources to the case of post-nonlinear mixtures is proposed. This is achieved by an adaptive algorithm in which the cost function jointly estimates the kurtosis and a measure of nonlinearity. Next, Kalman filtering is applied to blindly extract the signal of interest. The analysis of the proposed approach is conducted for the case of smooth post-nonlinear mixing and simulations are provided to illustrate both the quantitative and qualitative performance of the proposed algorithm.