• DOI:10.1155/ASP.2005.2400
• Corpus ID: 735566

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

@article{Bedini2004SeparationOC,
  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 Engin Kuruoglu and Anna Tonazzini},
  journal={EURASIP Journal on Advances in Signal Processing},
  year={2004},
  volume={2005},
  pages={1-13}
}

• L. Bedini, D. Herranz, +3 authors A. Tonazzini
• Published 6 July 2004
• Computer Science
• EURASIP Journal on Advances in Signal Processing

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.