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: Blind Separation of Nonstationary Sources Based on Spatial Time-Frequency Distributions | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 64785 Pages 1-13 DOI ASP 2006 64785 Blind Separation of Nonstationary Sources Based on Spatial Time-Frequency Distributions Yimin Zhang and Moeness G. Amin Wireless Communications and Positioning Lab Center for Advanced Communications Villanova University Villanova PA 19085 USA Received 1 January 2006 Revised 24 July 2006 Accepted 13 August 2006 Blind source separation BSS based on spatial time-frequency distributions STFDs provides improved performance over blind source separation methods based on second-order statistics when dealing with signals that are localized in the time-frequency t-f domain. In this paper we propose the use of STFD matrices for both whitening and recovery of the mixing matrix which are two stages commonly required in many BSS methods to provide robust BSS performance to noise. In addition a simple method is proposed to select the auto- and cross-term regions of time-frequency distribution TFD . To further improve the BSS performance t-f grouping techniques are introduced to reduce the number of signals under consideration and to allow the receiver array to separate more sources than the number of array sensors provided that the sources have disjoint t-f signatures. With the use of one or more techniques proposed in this paper improved performance of blind separation of nonstationary signals can be achieved. Copyright 2006 Y. Zhang and M. G. Amin. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION Several methods have been proposed to blindly separate independent narrowband sources 1-8 . When the spatial mixing signatures of the sources are not orthogonal blind source separation BSS methods usually employ at least two different sets of matrices that span the same
