1
Electerical Engineering, Isfahan University of Technology
2
Electerical and Computer Engineering, Isfahan University of Technology
Abstract
The separation of unobserved sources from mixed observed data is a fundamental signal processing problem. Most of the proposed techniques for solving this problem rely on independence or at least uncorrelation assumption for source signals. This paper introduces a 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.
Aghabozorgi, M. R., & Doost Hoseini, A. M. (2003). Blind Separation of Jointly Stationary Correlated Sources. International Journal of Engineering, 16(4), 331-346.
MLA
M. R. Aghabozorgi; A. M. Doost Hoseini. "Blind Separation of Jointly Stationary Correlated Sources". International Journal of Engineering, 16, 4, 2003, 331-346.
HARVARD
Aghabozorgi, M. R., Doost Hoseini, A. M. (2003). 'Blind Separation of Jointly Stationary Correlated Sources', International Journal of Engineering, 16(4), pp. 331-346.
VANCOUVER
Aghabozorgi, M. R., Doost Hoseini, A. M. Blind Separation of Jointly Stationary Correlated Sources. International Journal of Engineering, 2003; 16(4): 331-346.
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