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: Research Article A Complexity-Reduced ML Parametric Signal Reconstruction Method | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 875132 14 pages doi 2011 875132 Research Article A Complexity-Reduced ML Parametric Signal Reconstruction Method Z. Deprem 1 K. Leblebicioglu 2 O. Arikan 1 and A. E. Cetin1 1 Department of Electrical and Electronics Engineering Bilkent University Bilkent Ankara 06800 Turkey 2 Department of Electrical and Electronics Engineering Middle East Technical University Ankara 06531 Turkey Correspondence should be addressed to Z. Deprem zdeprem@ Received 2 September 2010 Revised 8 December 2010 Accepted 24 January 2011 Academic Editor Athanasios Rontogiannis Copyright 2011 Z. Deprem et al. 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. The problem of component estimation from a multicomponent signal in additive white Gaussian noise is considered. A parametric ML approach where all components are represented as a multiplication of a polynomial amplitude and polynomial phase term is used. The formulated optimization problem is solved via nonlinear iterative techniques and the amplitude and phase parameters for all components are reconstructed. The initial amplitude and the phase parameters are obtained via time-frequency techniques. An alternative method which iterates amplitude and phase parameters separately is proposed. The proposed method reduces the computational complexity and convergence time significantly. Furthermore by using the proposed method together with Expectation Maximization EM approach better reconstruction error level is obtained at low SNR. Though the proposed method reduces the computations significantly it does not guarantee global optimum. As is known these types of non-linear optimization algorithms converge to local minimum and do not guarantee global optimum. The
