Blind separation of jointly stationary correlated sources

The separation of unobserved sources from mixed observed data is a fundamental signal processing problem. Most proposed techniques for solving this problem rely on the independence of, or at least the assumption of uncorrelation of, the source signals. The paper introduces a technique for cases that the source signals are correlated with each other. The method uses the Wold decomposition principle for extracting the desired and proper information from the predictable part of the observed data, and exploits approaches based on second-order statistics to estimate the mixing matrix and source signals. Simulation results are provided to illustrate the effectiveness of the method.