Evaluation of classical and sparsity-based methods for parametric recovery problems

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2020

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Başkaya, Hasan Can

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Parametric reconstruction problems arise in many areas such as array processing, wireless communication, source separation, and spectroscopy. In a parametric recovery problem, the unknown model parameters in each superimposed signal are estimated from noisy observations. Classical methods perform the recovery over directly on the continuous-valued parameter space by solving a nonlinear inverse problem. Recently sparsity-based methods have also been applied to parametric recovery problems. These methods discretize the parameter space to form a dictionary whose atoms correspond to candidate parameter values, represent the data as a linear combination of small number of dictionary atoms, and then solve the resulting linear inverse problem. These sparsity-based methods can be classified into three categories, namely, on-grid, off-grid and gridless sparse methods. On-grid methods require that the true parameter values lie on a set of fixed grid points. Off-grid methods also use a grid, but the recovered parameter values are allowed to be out of the grid points. On the other hand, gridless methods do not require a grid and they work directly in the continuous-valued parameter space. In this thesis, we first review the classical and sparsity-based methods developed for parametric recovery problems with single or multiple measurement vectors. We then analyze and evaluate these methods in the direction-of- arrival and parameterized source separation problems.

Subject Keywords

Inverse problems (Differential equations), Keywords: parametric recovery, inverse problem, sparsity, block-sparsity, joint sparsity, single measurement vector, multiple measurement vector, direction-of-arrival, parameterized source separation.

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http://etd.lib.metu.edu.tr/upload/12625326/index.pdf
https://hdl.handle.net/11511/45278

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

H. C. Başkaya, “Evaluation of classical and sparsity-based methods for parametric recovery problems,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Electrical and Electronics Engineering., Middle East Technical University, 2020.