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

doi:10.1109/ICASSP.2015.7179056

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 proceeding › Conference contribution › peer-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 language	English
Title of host publication	2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5664-5668
Number of pages	5
ISBN (Electronic)	9781467369978
DOIs	https://doi.org/10.1109/ICASSP.2015.7179056
State	Published - 4 Aug 2015
Externally published	Yes
Event	40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia Duration: 19 Apr 2014 → 24 Apr 2014

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2015-August
ISSN (Print)	1520-6149

Conference

Conference	40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
Country/Territory	Australia
City	Brisbane
Period	19/04/14 → 24/04/14

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

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

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10.1109/ICASSP.2015.7179056

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Sardarabadi, A. M., Leshem, A., & Van Der Veen, A. J. (2015). Computationally efficient radio astronomical image formation using constrained least squares and and the MVDR beamformer. In 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings (pp. 5664-5668). Article 7179056 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2015-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2015.7179056

Sardarabadi, A. Mouri ; Leshem, A. ; Van Der Veen, A. J. / Computationally efficient radio astronomical image formation using constrained least squares and and the MVDR beamformer. 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 5664-5668 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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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.",

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author = "Sardarabadi, {A. Mouri} and A. Leshem and {Van Der Veen}, {A. J.}",

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Sardarabadi, AM, Leshem, A & Van Der Veen, AJ 2015, Computationally efficient radio astronomical image formation using constrained least squares and and the MVDR beamformer. in 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings., 7179056, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2015-August, Institute of Electrical and Electronics Engineers Inc., pp. 5664-5668, 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015, Brisbane, Australia, 19/04/14. https://doi.org/10.1109/ICASSP.2015.7179056

Computationally efficient radio astronomical image formation using constrained least squares and and the MVDR beamformer. / Sardarabadi, A. Mouri; Leshem, A.; Van Der Veen, A. J.
2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 5664-5668 7179056 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2015-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Sardarabadi AM, Leshem A, Van Der Veen AJ. Computationally efficient radio astronomical image formation using constrained least squares and and the MVDR beamformer. In 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 5664-5668. 7179056. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2015.7179056

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

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Bibliographical note

Keywords

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