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.

翻译：本文提出一种基于copula统计的线性混合相依源盲源分离方法，该方法通过表征源分量信号间非线性依赖关系的copula密度函数进行建模。假设源信号为平稳过程，该算法通过最小化估计源信号copula密度函数与依赖结构对应的kl散度来实现分离。将所提方法应用于经典11母线4机系统时域分析数据，大量仿真结果表明，基于copula统计的方法在干扰信号比指标上收敛更快，且优于现有最优的相依源盲源分离方法。