Radioastronomical image reconstruction with regularized least squares

Radioastronomical image reconstruction with regularized least squares

Naghibzadeh, S. (TU Delft Signal Processing Systems)
Mouri Sardarabadi, A. (TU Delft Signal Processing Systems)
van der Veen, A.J. (TU Delft Signal Processing Systems)

Dong, Min (editor)
Zheng, Thomas Fang (editor)

2016-03

Image formation using the data from an array of sensors is a familiar problem in many fields such as radio astronomy, biomedical and geodetic imaging. The problem can be formulated as a least squares (LS) estimation problem and becomes ill-posed at high resolutions, i.e. large number of image pixels. In this paper we propose two regularization methods, one based on weighted truncation of the eigenvalue decomposition of the image deconvolution matrix and the other based on the prior knowledge of the "dirty image" using the available data. The methods are evaluated by simulations as well as actual data from a phased-array radio telescope in the Netherlands, the Low Frequency Array Radio Telescope (LOFAR).

radio astronomy
Array signal processing
image formation
interferometry
regularization

http://resolver.tudelft.nl/uuid:c049f07f-037e-434d-b834-1178fc669a3b

https://doi.org/10.1109/icassp.2016.7472291

IEEE, Danvers, MA

978-1-4799-9988-0

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings

2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016, 2016-03-20 → 2016-03-25, Shanghai International Convention Center, Shanghai, China

Accepted Author Manuscript

Institutional Repository

conference paper

© 2016 S. Naghibzadeh, A. Mouri Sardarabadi, A.J. van der Veen