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Cramér-Rao-Induced Bound for Blind Separation of Stationary Parametric Gaussian Sources

The performance of blind source separation algorithms is commonly measured by the output interference-to-signal ratio (ISR). In this paper, we derive an asymptotic bound on the attainable ISR for the case of Gaussian parametric auto-regressive (AR), moving-average (MA), or auto-regressive moving-ave...

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Bibliographic Details
Published in:IEEE signal processing letters 2007-06, Vol.14 (6), p.417-420
Main Authors: Doron, E., Yeredor, A., Tichavsky, P.
Format: Article
Language:English
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Summary:The performance of blind source separation algorithms is commonly measured by the output interference-to-signal ratio (ISR). In this paper, we derive an asymptotic bound on the attainable ISR for the case of Gaussian parametric auto-regressive (AR), moving-average (MA), or auto-regressive moving-average (ARMA) processes. Our bound is induced by the Crameacuter-Rao bound on estimation of the mixing matrix. We point out the relation to some previously obtained results, and provide a concise expression with some associated important insights. Using simulation, we demonstrate that the bound is attained asymptotically by some asymptotically efficient algorithms
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2006.888425