Fast and accurate radio interferometric imaging using krylov subspaces

Shahrzad Naghibzadeh, Alle Jan Van Der Veen

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

Abstract

We propose a fast iterative method for image formation in Radio Astronomy (RA). We formulate the image formation problem as a maximum likelihood estimation problem to estimate the image pixel powers via array covariance measurements. We use an iterative solution method based on projections onto Krylov subspaces and exploit the sample covariance error estimate via discrepancy principle as the stopping criterion. We propose to regularize the ill-posed imaging problem based on a Bayesian framework using MVDR beamformed data applied as a right preconditioner to the system matrix. We compare the proposed method with the state-of-the-art sparse sensing methods and show that the proposed method obtains comparably accurate solutions with a significant reduction in computation.

Original languageEnglish
Title of host publication2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
Place of PublicationPiscataway
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Electronic)978-1-5386-1251-4
ISBN (Print)978-1-5386-1252-1
DOIs
Publication statusPublished - 2018
Event2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Willemstad, Curaçao
Duration: 10 Dec 201713 Dec 2017
Conference number: 7
http://www.cs.huji.ac.il/conferences/CAMSAP17/

Workshop

Workshop2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Abbreviated titleCAMSAP
CountryCuraçao
CityWillemstad
Period10/12/1713/12/17
Internet address

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