Computationally efficient radio astronomical image formation using constrained least squares and and the MVDR beamformer

A. Mouri Sardarabadi*, A. Leshem, A. J. Van Der Veen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Linear image deconvolution for radio-astronomy is an ill-posed problem. For this reason, a-priori knowledge is crucial for improving the performance of the deconvolution. In this paper we show that combining non-negativity constraints with an upper bound on the magnitude of each pixel in the image can significantly improve the image formation algorithm. We also show that the minimum variance distortionless response (MVDR) dirty image provides the tightest upper bound out of all beamformers. We then show how the LS-MVI image formation algorithm can be reformulated as a preconditioned weighted least squares algorithm. The resulting algorithm can be efficiently solved using the active-set method. The performance of the algorithm is demonstrated in simulation and compared with constrained least squares based on the classical dirty image.

Original languageAmerican English
Title of host publication2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5664-5668
Number of pages5
ISBN (Electronic)9781467369978
DOIs
StatePublished - 4 Aug 2015
Externally publishedYes
Event40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
Duration: 19 Apr 201424 Apr 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2015-August
ISSN (Print)1520-6149

Conference

Conference40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
Country/TerritoryAustralia
CityBrisbane
Period19/04/1424/04/14

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

  • Krylov subspace
  • LSQR
  • MVDR
  • Radio astronomy
  • array signal processing
  • constrained optimization
  • image deconvolution

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