tailieunhanh - Báo cáo hóa học: " Separation of Correlated Astrophysical Sources Using Multiple-Lag Data Covariance Matrices"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Separation of Correlated Astrophysical Sources Using Multiple-Lag Data Covariance Matrices | EURASIP Journal on Applied Signal Processing 2005 15 2400-2412 2005 Hindawi Publishing Corporation Separation of Correlated Astrophysical Sources Using Multiple-Lag Data Covariance Matrices L. Bedini Istituto di Scienza e Tecnologie dell Informazione CNR Area della Ricerca di Pisa via G. Moruzzi 1 56124 Pisa Italy Email D. Herranz Istituto di Scienza e Tecnologie dell Informazione CNR Area della Ricerca di Pisa via G. Moruzzi 1 56124 Pisa Italy Email munoz@ E. Salerno Istituto di Scienza e Tecnologie dell Informazione CNR Area della Ricerca di Pisa via G. Moruzzi 1 56124 Pisa Italy Email C. Baccigalupi International School for Advanced Studies via Beirut 4 34014 Trieste Italy Email bacci@ E. E. Kuruogiu Istituto di Scienza e Tecnologie dell Informazione CNR Area della Ricerca di Pisa via G. Moruzzi 1 56124 Pisa Italy Email A. Tonazzini Istituto di Scienza e Tecnologie dell Informazione CNR Area della Ricerca di Pisa via G. Moruzzi 1 56124 Pisa Italy Email Received 8 June 2004 Revised 18 October 2004 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 .