Cramér-Rao-induced bound for blind separation of stationary parametric Gaussian sources

Eran Doron, Arie Yeredor, Petr Tichavský

Research output: Contribution to journalArticlepeer-review


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 Cramér-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.

Original languageEnglish
Pages (from-to)417-420
Number of pages4
JournalIEEE Signal Processing Letters
Issue number6
StatePublished - Jun 2007


  • Auto-regressive (AR)
  • Auto-regressive moving average (ARMA)
  • Blind source separation (BSS)
  • Cramer-Rao bound
  • Independent component analysis (ICA)
  • Interference-to-signal ratio (ISR)
  • Moving average (MA)


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