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: Algorithms for Blind Components Separation and Extraction from the Time-Frequency Distribution of Their Mixture | EURASIP Journal on Applied Signal Processing 2004 13 2025-2033 2004 Hindawi Publishing Corporation Algorithms for Blind Components Separation and Extraction from the Time-Frequency Distribution of Their Mixture B. Barkat School of Electrical and Electronic Engineering Nanyang Technological University Nanyang Avenue 639798 Singapore Email ebarkat@ K. Abed-Meraim Signal and Image Processing Department Ecole National Superieure des Telecommunications Telecom Paris 75013 Paris France Email abed@ Received 20 February 2003 Revised 29 November 2003 Recommended for Publication by Petar Djuric We propose novel algorithms to select and extract separately all the components using the time-frequency distribution TFD of a given multicomponent frequency-modulated FM signal. These algorithms do not use any a priori information about the various components. However their performances highly depend on the cross-terms suppression ability and high time-frequency resolution of the considered TFD. To illustrate the usefulness of the proposed algorithms we applied them for the estimation of the instantaneous frequency coefficients of a multicomponent signal and the results are compared with those of the higher-order ambiguity function HAF algorithm. Monte Carlo simulation results show the superiority of the proposed algorithms over the HAF. Keywords and phrases time-frequency signal analysis components separation polynomial phase signals instantaneous frequency estimation. 1. INTRODUCTION The joint time-frequency analysis has proved to be a powerful tool in the analysis of nonstationary signals that is signals whose spectral contents vary with time 1 . Such signals may be found in many engineering applications such as radar sonar telecommunications and biomedical engineering. These signals can be classified in two groups monocomponent and multicomponent. In this paper we focus our analysis on multicomponent signals. By a multicomponent signal we mean a signal whose
