Using higher order statistics and time structure to separate source signals
Peng Li, Rui Li · 2010
The aim of this paper is to solve the blind source separation (BSS) problem using the temporal independent component analysis (ICA) model. In contrast to ordinary ICA, except for independent assumption, the temporal structure of the source components is taken into account. After combing the virtues of both high order statistics and the temporal second-order information of the source signals, we can get the novel strengthened BSS algorithm-STICA algorithm by using the joint approximate diagonalisation of eigen-matrices (JADE) method. The proposed STICA can not only separate the spatial independent random variables but also the spatial independent time series or both of them exist simultaneously, especially when the sources have non-symmetric distributed time series.