Point and Beam-Sparse Radio Astronomical Source Recovery Using Non-Negative Least Squares

Conference paper (2016)

Authors

S. Naghibzadeh Signal Processing Systems -

A. Mouri Sardarabadi Signal Processing Systems -

A.J. van der Veen Signal Processing Systems -

Research Group

Signal Processing Systems () (TU Delft)

Regularization Interferometry Radio astronomy Array signal processing Image formation Non-negative least squares

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http://resolver.tudelft.nl/uuid:ff9e9a23-c4a6-4afa-9635-985d309eb37c

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Published Date

19-09-2016

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

Abstract

A simple and novel algorithm for source recovery based on array data measurements in radio astronomy is proposed. Considering that a radioastronomical image is composed of both point sources and extended emissions, prior information on the images, namely non-negativity and substantial black background are taken into account to choose source representation basis functions. Dirac delta functions are chosen to represent point sources and a Gaussian function approximated from the main beam of the antenna array is selected to capture the extended emissions. We apply the non-negative least squares (NNLS) algorithm to estimate the basis coefficients. It is shown that the sparsity promoted by the NNLS algorithm based on the chosen basis functions results in a super-resolution (finer resolution than prescribed by the main beam of the antenna array pattern) estimate for the point sources and smooth recovery for the extended emissions.