Radioastronomical least squares image reconstruction with iteration regularized Krylov subspaces and beamforming-based prior conditioning

Shahrzad Naghibzadeh, Alle-Jan van der Veen

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

3 Citations (Scopus)

Abstract

A fast iterative method based on projection onto Krylov subspaces has been proposed for Radio Astronomical (RA) image reconstruction from telescope array measurements. The image formation problem is formulated as a linear least squares (LS) estimation problem by discretizing the Field of View (FoV) of the telescope array into a number of pixels. The ill-posed imaging problem is regularized by the Krylov iterations and the system matrix is prior conditioned by the weights attained from the matched filter beamformed data. The performance of the proposed method is shown based on simulated data from a single station of the the Low Frequency Array Radio Telescope (LOFAR) antenna configuration on a test radio astronomical image. It has been shown that the prior conditioning of the system matrix results in a more accurate image estimate by reducing the artifacts introduced in the empty parts of the image. Furthermore, it was shown that Krylov-based methods fit very well in the context of large scale RA image reconstruction due to their speed and computational benefits.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
Subtitle of host publicationICASSP 2017
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages3385-3389
Number of pages5
ISBN (Electronic)978-1-5090-4117-6
DOIs
Publication statusPublished - 2017
EventICASSP 2017: 42nd IEEE International Conference on Acoustics, Speech and Signal Processing - The Internet of Signals - Hilton New Orleans Riverside, New Orleans, LA, United States
Duration: 5 Mar 20179 Mar 2017
Conference number: 42
http://www.ieee-icassp2017.org/

Conference

ConferenceICASSP 2017
Abbreviated titleICASSP
CountryUnited States
CityNew Orleans, LA
Period5/03/179/03/17
Internet address

Keywords

  • Image formation
  • interferometry
  • regularization
  • radio astronomy
  • Krylov-based imaging

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    Naghibzadeh, S., & van der Veen, A-J. (2017). Radioastronomical least squares image reconstruction with iteration regularized Krylov subspaces and beamforming-based prior conditioning. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings: ICASSP 2017 (pp. 3385-3389). IEEE. https://doi.org/10.1109/ICASSP.2017.7952784