This paper proposes a new strategy to separate astrophysical sources that are mutually correlated. This strategy is based on second-order 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 about the mixing matrix, our strategy allows the independence assumption to be relaxed and performs the separation of even significantly correlated sources. Besides the mixing matrix, our strategy is also capable to evaluate the source covariance functions at several lags. Moreover, once the mixing parameters have been identified, a simple deconvolution can be used to estimate the probability density functions of the source processes. To benchmark our algorithm, we used a database that simulates the one expected from the instruments that will operate onboard ESA's Planck Surveyor Satellite to measure the CMB anisotropies all over the celestial sphere.
This article is part of the series Applications of Signal Processing in Astrophysics and Cosmology.
Separation of Correlated Astrophysical Sources Using Multiple-Lag Data Covariance Matrices
1 Istituto di Scienza e Tecnologie dell' Informazione, CNR, Area della Ricerca di Pisa, via G. Moruzzi 1, Pisa 56124, Italy
2 International School for Advanced Studies, via Beirut 4, Trieste 34014, Italy
EURASIP Journal on Advances in Signal Processing 2005, 2005:190845 doi:10.1155/ASP.2005.2400
The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2005/15/190845
|Received:||8 June 2004|
|Revisions received:||18 October 2004|
|Published:||14 September 2005|
© 2005 Bedini et al.