A complexity-reduced ML parametric signal reconstruction method
Author
Deprem, Z.
Leblebicioglu, K.
Arkan O.
Çetin, A.E.
Date
2011Source Title
Eurasip Journal on Advances in Signal Processing
Print ISSN
16876172
Volume
2011
Language
English
Type
ArticleItem Usage Stats
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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.
Keywords
Additive White Gaussian noiseAlternative 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