Print Email Facebook Twitter Bayesian Learning Applied to Radio Astronomy Image Formation Title Bayesian Learning Applied to Radio Astronomy Image Formation Author Tang, Yajie (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor van der Veen, Alle-Jan (mentor) Degree granting institution Delft University of Technology Programme Electrical Engineering | Circuits and Systems Date 2019-11-11 Abstract Radio astronomy image formation can be treated as a linear inverse problem. However, due to physical limitations, this inverse problem is ill-posed. To overcome the ill-posedness, side information should be involved. Based on the sparsity assumption of the sky image, we involve l1-regularization. We formulate the image formation problem into a l1-regularized weighted least square (WLS) problem and associate each variable with one regularization parameter. We use Bayesian learning to learn the regularization parameters from data by maximizing the posterior density. With the iterative update of the regularization parameters, the solution is updated until convergence of the regularization parameters. We involve a stopping rule based on the noise level to improve the computational efficiency and control the sparsity of the solution. We compare the performance of this Bayesian learning method with other existing imaging methods by simulations. Finally, we propose some future research directions in improving the performance of this Bayesian learning method. Subject Bayesian learningRadio astronomySparsity To reference this document use: http://resolver.tudelft.nl/uuid:eebfc5bb-1393-477c-8437-8e51457ae6d7 Part of collection Student theses Document type master thesis Rights © 2019 Yajie Tang Files PDF Bayesian_Learning_Applied ... mation.pdf 3.89 MB Close viewer /islandora/object/uuid:eebfc5bb-1393-477c-8437-8e51457ae6d7/datastream/OBJ/view