Blind source separation using Wold decomposition and second order statistics
Masoud Reza Aghabozorgi Sahaf · International Conference on Applied Mathematics · 2005
The separation of unobserved sources from mixed observed data is a fundamental signal processing problem. Most proposed techniques for solving this problem rely on independence or at least uncorrelation assumption of source signals. This paper introduces a novel technique for cases that source signals are correlated with each other. The method uses Wold decomposition principle for extracting 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.