Cramér–Rao-Induced Bound for Blind Separation of Stationary Parametric Gaussian Sources
Eran Doron, Arie Yeredor, Petr Tichavský · IEEE Signal Processing Letters · 2007
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