A Complexity-Reduced ML Parametric Signal Reconstruction Method

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2011-01-01

Author

Deprem, Z.
Leblebicioğlu, Mehmet Kemal
ARIKAN, Orhan
ÇETİN, AHMET ENİS

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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.

URI

https://hdl.handle.net/11511/41263

Journal

EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING

DOI

https://doi.org/10.1155/2011/875132

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

Z. Deprem, M. K. Leblebicioğlu, O. ARIKAN, and A. E. ÇETİN, “A Complexity-Reduced ML Parametric Signal Reconstruction Method,” EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, pp. 0–0, 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41263.