Separation of Correlated Astrophysical Sources Using Multiple-Lag Data Covariance Matrices

Abstract

This paper proposes a new strategy to separate astrophysical sources that are mutually correlated. This strategy is based on secondorder statistics and exploits prior information about the possible structure of the mixing matrix. Unlike ICA blind separation approaches, where the sources are assumed mutually independent and no prior knowledge is assumed… (More)
DOI: 10.1155/ASP.2005.2400

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@article{Bedini2005SeparationOC, title={Separation of Correlated Astrophysical Sources Using Multiple-Lag Data Covariance Matrices}, author={Luigi Bedini and Diego Herranz and Emanuele Salerno and Carlo Baccigalupi and Ercan E. Kuruoglu and Anna Tonazzini}, journal={EURASIP J. Adv. Sig. Proc.}, year={2005}, volume={2005}, pages={2400-2412} }