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

Author

Doron, Eran ; Yeredor, Arie ; Tichavský, Petr

Author_Institution

Sch. of Electr. Eng., Tel-Aviv Univ.

Volume

14

Issue

6

fYear

2007

fDate

6/1/2007 12:00:00 AM

Firstpage

417

Lastpage

420

Abstract

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

Keywords

Gaussian processes; autoregressive moving average processes; blind source separation; matrix algebra; ARMA; Cramer-Rao-induced bound; autoregressive moving-average process; blind source separation algorithm; mixing matrix estimation; stationary parametric Gaussian sources; Automation; Blind source separation; Estimation error; Helium; Independent component analysis; Information theory; Interference; Pollution measurement; Signal processing algorithms; Source separation; Auto-regressive (AR); Cramer–Rao bound; auto-regressive moving average (ARMA); blind source separation (BSS); independent component analysis (ICA); interference-to-signal ratio (ISR); moving average (MA);

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2006.888425

Filename

4202618