A Complexity-Reduced ML Parametric Signal Reconstruction Method

Download

index.pdf

Date

2011-01-01

Author

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

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

144
views

101
downloads

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

Suggestions

OpenMETU
Core

A fine-resolution frequency estimator using an arbitrary number of DFT coefficients Orguner, Umut; Candan, Çağatay (2014-12-01) A method for the frequency estimation of complex exponential signals observed under additive white Gaussian noise is presented. Unlike competing methods based on relatively few Discrete Fourier Transform (DFT) samples, the presented technique can generate a frequency estimate by fusing the information from all DFT samples. The estimator is shown to follow the Cramer-Rao bound with a smaller signal-to-noise ratio (SNR) gap than the competing estimators at high SNR.
A financial cost metric for result caching Sazoglu, Fethi Burak; Cambazoglu, B. Barla; Ozcan, Rifat; Altıngövde, İsmail Sengör; Ulusoy, Özgür (2013-09-02) Interpolation is an important problem of signal processing. Even though there are several methods for interpolation, it is still an open problem. In this paper, we propose a new method for interpolation with certain advantages compared to the previous methods. The proposed method is based on the least squares error optimum design of the interpolating filter. Interpolating filter is chosen as the Kaiser filter since it can be configured in a variety of shapes by the appropriate choice of cut-off and shape pa...
A Method For Fine Resolution Frequency Estimation From Three DFT Samples Candan, Çağatay (2011-06-01) The parameter estimation of a complex exponential waveform observed under white noise is typically tackled in two stages. In the first stage, a coarse frequency estimate is found by the application of an N-point DFT to the input of length N. In the second stage, a fine search around the peak determined in the first stage is conducted. The method proposed in this paper presents a simpler alternative. The method suggests a nonlinear relation involving three DFT samples already calculated in the first stage to...
A new method for signal interpolation Tuncer, Temel Engin (2006-04-19) Interpolation is an important problem of signal processing. Even though there are several methods for interpolation, it is still an open problem. In this paper, we propose a new method for interpolation with certain advantages compared to the previous methods. The proposed method is based on the least squares error optimum design of the interpolating filter. Interpolating filter is chosen as the Kaiser filter since it can be configured in a variety of shapes by the appropriate choice of cut-off and shape pa...
A simplified MAP channel estimator for OFDM systems under Rayleigh fading ÇÜRÜK, SELVA; Tanık, Yalçın (2010-06-01) This paper presents a simplified Maximum A Posteriori (SMAP) channel estimator to be used in orthogonal frequency division multiplexing (OFDM) systems under the Rayleigh fading assumption for the subchannels, using a parametric correlation model and assuming that the channel is frequency selective and slowly time varying. Expressions for the mean-square error (MSE) of estimations are derived to evaluate the performance of the estimator. The relation between the correlation of subchannels taps and error vari...

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.