Open Access

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

  • L. Bedini1Email author,
  • D. Herranz1,
  • E. Salerno1,
  • C. Baccigalupi2,
  • E.E. Kuruoğlu1 and
  • A. Tonazzini1
EURASIP Journal on Advances in Signal Processing20052005:190845

DOI: 10.1155/ASP.2005.2400

Received: 8 June 2004

Published: 14 September 2005

Abstract

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.

Keywords and phrases:

statistical image processing cosmic microwave background

Authors’ Affiliations

(1)
Istituto di Scienza e Tecnologie dell' Informazione, CNR
(2)
International School for Advanced Studies

Copyright

© Bedini et al. 2005