Fast and accurate radio interferometric imaging using krylov subspaces

Shahrzad Naghibzadeh; Alle Jan Van Der Veen

doi:10.1109/CAMSAP.2017.8313147

Fast and accurate radio interferometric imaging using krylov subspaces

Shahrzad Naghibzadeh, Alle Jan Van Der Veen

Signal Processing Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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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 language	English
Title of host publication	2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
Place of Publication	Piscataway
Publisher	IEEE
Pages	1-5
Number of pages	5
ISBN (Electronic)	978-1-5386-1251-4
ISBN (Print)	978-1-5386-1252-1
DOIs	https://doi.org/10.1109/CAMSAP.2017.8313147
Publication status	Published - 2018
Event	2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Willemstad, Curaçao Duration: 10 Dec 2017 → 13 Dec 2017 Conference number: 7 http://www.cs.huji.ac.il/conferences/CAMSAP17/

Workshop

Workshop	2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Abbreviated title	CAMSAP
Country/Territory	Curaçao
City	Willemstad
Period	10/12/17 → 13/12/17
Internet address	http://www.cs.huji.ac.il/conferences/CAMSAP17/

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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title = "Fast and accurate radio interferometric imaging using krylov subspaces",

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.",

author = "Shahrzad Naghibzadeh and {Van Der Veen}, {Alle Jan}",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. ; 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP ; Conference date: 10-12-2017 Through 13-12-2017",

year = "2018",

doi = "10.1109/CAMSAP.2017.8313147",

language = "English",

isbn = "978-1-5386-1252-1",

pages = "1--5",

booktitle = "2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017",

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url = "http://www.cs.huji.ac.il/conferences/CAMSAP17/",

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Naghibzadeh, S & Van Der Veen, AJ 2018, Fast and accurate radio interferometric imaging using krylov subspaces. in 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017. IEEE, Piscataway, pp. 1-5, 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Willemstad, Curaçao, 10/12/17. https://doi.org/10.1109/CAMSAP.2017.8313147

Fast and accurate radio interferometric imaging using krylov subspaces. / Naghibzadeh, Shahrzad; Van Der Veen, Alle Jan.
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017. Piscataway: IEEE, 2018. p. 1-5.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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N2 - 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.

AB - 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.

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Fast and accurate radio interferometric imaging using krylov subspaces

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