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Open Access

A Complexity-Reduced ML Parametric Signal Reconstruction Method

EURASIP Journal on Advances in Signal Processing20112011:875132

Received: 2 September 2010

Accepted: 24 January 2011

Published: 20 February 2011


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 global optimum is initialization dependent.


Global OptimumExpectation MaximizationAdditive White Gaussian NoiseReconstruction ErrorConvergence Time

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Authors’ Affiliations

Department of Electrical and Electronics Engineering, Bilkent University, Bilkent, Ankara, Turkey
Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey


© Z. Deprem et al. 2011

This article is published under license to BioMed Central Ltd. 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.