Blind separation of Gaussian sources with general covariance structures: Bounds and optimal estimation

Arie Yeredor*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We consider the separation of Gaussian sources exhibiting general, arbitrary (not necessarily stationary) covariance structures. First, assuming a semi-blind scenario, in which the sources' covariance structures are known, we derive the maximum likelihood estimate of the separation matrix, as well as the induced CramrRao lower bound (iCRLB) on the attainable Interference to Source Ratio (ISR). We then extend our results to the fully blind scenario, in which the covariance structures are unknown. We show that (under a scaling convention) the Fisher information matrix in this case is block-diagonal, implying that the same iCRLB (as in the semi-blind scenario) applies in this case as well. Subsequently, we demonstrate that the same semi-blind optimal performance can be approached asymptotically in the fully blind scenario if the sources are sufficiently ergodic, or if multiple snapshots are available.

Original languageEnglish
Article number5491131
Pages (from-to)5057-5068
Number of pages12
JournalIEEE Transactions on Signal Processing
Volume58
Issue number10
DOIs
StatePublished - Oct 2010

Keywords

  • Blind source separation
  • independent component analysis
  • nonstationarity
  • second-order statistics
  • time-varying AR processes

Fingerprint

Dive into the research topics of 'Blind separation of Gaussian sources with general covariance structures: Bounds and optimal estimation'. Together they form a unique fingerprint.

Cite this