A complexity-reduced ML parametric signal reconstruction method

Date
2011
Authors
Deprem, Z.
Leblebicioglu, K.
Arkan O.
Çetin, A.E.
Advisor
Instructor
Source Title
Eurasip Journal on Advances in Signal Processing
Print ISSN
16876172
Electronic ISSN
Publisher
Volume
2011
Issue
Pages
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
Abstract

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. © 2011 Z. Deprem et al.

Course
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Book Title
Keywords
Additive White Gaussian noise, Alternative methods, Component estimation, Convergence time, Expectation-maximization approaches, Global optimum, Iterative technique, Local minimums, Low SNR, Multicomponent signals, Non-linear optimization algorithms, Optimization problems, Phase parameters, Polynomial phase, Reconstruction error, Time-frequency techniques, Gaussian noise (electronic), Iterative methods, Optimization, White noise, Computational complexity
Citation
Published Version (Please cite this version)