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Báo cáo hóa học: " Separating More Sources Than Sensors Using Time-Frequency Distributions"

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: Separating More Sources Than Sensors Using Time-Frequency Distributions | EURASIP Journal on Applied Signal Processing 2005 17 2828-2847 2005 Hindawi Publishing Corporation Separating More Sources Than Sensors Using Time-Frequency Distributions Nguyen Linh-Trung Service Systeme Telecommunications Spatiales Centre National d Etudes Spatiales 18 avenue Edouard Belin 31401 Toulouse France Email linhtrung@ieee.org Adel Belouchrani Departement d Electronique Ecole Nationale Polytechnique 10 avenue Hassen Badi PB 182 EL Harrach 16200 Algiers Algeria Email adel.belouchrani@mail.enp.edu.dz Karim Abed-Meraim Departement Traitement du Signal et des Images Ecole Nationale Superieure des Telecommunications 46 rue Barrault 75634 Paris Cedex 13 France Email abed@tsi.enst.fr Boualem Boashash College of Engineering University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates Email boualem_boashash@yahoo.com Received 8 July 2004 Revised 24 March 2005 Recommended for Publication by Kostas Berberidis We examine the problem of blind separation of nonstationary sources in the underdetermined case where there are more sources than sensors. Since time-frequency TF signal processing provides effective tools for dealing with nonstationary signals we propose a new separation method that is based on time-frequency distributions TFDs . The underlying assumption is that the original sources are disjoint in the time-frequency TF domain. The successful method recovers the sources by performing the following four main procedures. First the spatial time-frequency distribution STFD matrices are computed from the observed mixtures. Next the auto-source TF points are separated from cross-source TF points thanks to the special structure of these mixture STFD matrices. Then the vectors that correspond to the selected auto-source points are clustered into different classes according to the spatial directions which differ among different sources each class now containing the auto-source points of only one source gives an estimation of the TFD of this source. Finally the .