In this paper, we propose a blind source separation of a linear mixture of
dependent sources based on copula statistics that measure the non-linear
dependence between source component signals structured as copula density
functions. The source signals are assumed to be stationary. The method
minimizes the Kullback-Leibler divergence between the copula density functions
of the estimated sources and of the dependency structure. The proposed method
is applied to data obtained from the time-domain analysis of the classical
11-Bus 4-Machine system. Extensive simulation results demonstrate that the
proposed method based on copula statistics converges faster and outperforms the
state-of-the-art blind source separation method for dependent sources in terms
of interference-to-signal ratio.Comment: Submitted to the ISGT NA 202