Radioastronomical image reconstruction with regularized least squares

Shahrzad Naghibzadeh, Ahmad Mouri Sardarabadi, Alle-Jan van der Veen

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

5 Citations (Scopus)
34 Downloads (Pure)

Abstract

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).
Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Subtitle of host publicationProceedings
EditorsMin Dong, Thomas Fang Zheng
Place of PublicationDanvers, MA
PublisherIEEE
Pages3316-3320
Number of pages5
ISBN (Electronic)978-1-4799-9988-0
DOIs
Publication statusPublished - Mar 2016
Event2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai International Convention Center, Shanghai, China
Duration: 20 Mar 201625 Mar 2016

Conference

Conference2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Abbreviated titleICASSP
CountryChina
CityShanghai
Period20/03/1625/03/16

Keywords

  • radio astronomy
  • Array signal processing
  • image formation
  • interferometry
  • regularization

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