Point and beam-sparse radio astronomical source recovery using non-negative least squares
| Autor: | Alle-Jan van der Veen, Shahrzad Naghibzadeh, Ahmad Mouri Sardarabadi |
|---|---|
| Přispěvatelé: | Astronomy |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Image formation
Mathematical optimization Dirac delta function 020206 networking & telecommunications Basis function 02 engineering and technology interferometry radio astronomy 01 natural sciences Antenna array regularization Interferometry symbols.namesake Non-negative least squares 0103 physical sciences Array signal processing non-negative least squares 0202 electrical engineering electronic engineering information engineering symbols Gaussian function image formation 010303 astronomy & astrophysics Algorithm Mathematics Radio astronomy |
| Zdroj: | SAM 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2016, 2016-September |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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