Radioastronomical image reconstruction with regularized least squares

Shahrzad Naghibzadeh; Ahmad Mouri Sardarabadi; Alle-Jan van der Veen

doi:10.1109/icassp.2016.7472291

Radioastronomical image reconstruction with regularized least squares

Shahrzad Naghibzadeh, Ahmad Mouri Sardarabadi, Alle-Jan van der Veen

Signal Processing Systems

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

5 Citations (Scopus)

79 Downloads (Pure)

Abstract

Image formation using the data from an array of sensors is a familiar problem in many fields such as radio astronomy, biomedical and geodetic imaging. The problem can be formulated as a least squares (LS) estimation problem and becomes ill-posed at high resolutions, i.e. large number of image pixels. In this paper we propose two regularization methods, one based on weighted truncation of the eigenvalue decomposition of the image deconvolution matrix and the other based on the prior knowledge of the "dirty image" using the available data. The methods are evaluated by simulations as well as actual data from a phased-array radio telescope in the Netherlands, the Low Frequency Array Radio Telescope (LOFAR).

Original language	English
Title of host publication	2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Subtitle of host publication	Proceedings
Editors	Min Dong, Thomas Fang Zheng
Place of Publication	Danvers, MA
Publisher	IEEE
Pages	3316-3320
Number of pages	5
ISBN (Electronic)	978-1-4799-9988-0
DOIs	https://doi.org/10.1109/icassp.2016.7472291
Publication status	Published - Mar 2016
Event	2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai International Convention Center, Shanghai, China Duration: 20 Mar 2016 → 25 Mar 2016

Conference

Conference	2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Abbreviated title	ICASSP
Country/Territory	China
City	Shanghai
Period	20/03/16 → 25/03/16

Bibliographical note

Accepted Author Manuscript

Keywords

radio astronomy
Array signal processing
image formation
interferometry
regularization

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10.1109/icassp.2016.7472291

aj16icassp2Accepted author manuscript, 224 KB

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title = "Radioastronomical image reconstruction with regularized least squares",

abstract = "Image formation using the data from an array of sensors is a familiar problem in many fields such as radio astronomy, biomedical and geodetic imaging. The problem can be formulated as a least squares (LS) estimation problem and becomes ill-posed at high resolutions, i.e. large number of image pixels. In this paper we propose two regularization methods, one based on weighted truncation of the eigenvalue decomposition of the image deconvolution matrix and the other based on the prior knowledge of the {"}dirty image{"} using the available data. The methods are evaluated by simulations as well as actual data from a phased-array radio telescope in the Netherlands, the Low Frequency Array Radio Telescope (LOFAR).",

keywords = "radio astronomy, Array signal processing, image formation, interferometry, regularization",

author = "Shahrzad Naghibzadeh and {Mouri Sardarabadi}, Ahmad and {van der Veen}, Alle-Jan",

note = "Accepted Author Manuscript; 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016, ICASSP ; Conference date: 20-03-2016 Through 25-03-2016",

year = "2016",

month = mar,

doi = "10.1109/icassp.2016.7472291",

language = "English",

pages = "3316--3320",

editor = "Min Dong and Zheng, {Thomas Fang}",

booktitle = "2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",

publisher = "IEEE",

address = "United States",

}

Naghibzadeh, S, Mouri Sardarabadi, A & van der Veen, A-J 2016, Radioastronomical image reconstruction with regularized least squares. in M Dong & TF Zheng (eds), 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings. IEEE, Danvers, MA, pp. 3316-3320, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016, Shanghai, China, 20/03/16. https://doi.org/10.1109/icassp.2016.7472291

Radioastronomical image reconstruction with regularized least squares. / Naghibzadeh, Shahrzad; Mouri Sardarabadi, Ahmad; van der Veen, Alle-Jan.
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings. ed. / Min Dong; Thomas Fang Zheng. Danvers, MA: IEEE, 2016. p. 3316-3320.

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

TY - GEN

T1 - Radioastronomical image reconstruction with regularized least squares

AU - Naghibzadeh, Shahrzad

AU - Mouri Sardarabadi, Ahmad

AU - van der Veen, Alle-Jan

N1 - Accepted Author Manuscript

PY - 2016/3

Y1 - 2016/3

N2 - Image formation using the data from an array of sensors is a familiar problem in many fields such as radio astronomy, biomedical and geodetic imaging. The problem can be formulated as a least squares (LS) estimation problem and becomes ill-posed at high resolutions, i.e. large number of image pixels. In this paper we propose two regularization methods, one based on weighted truncation of the eigenvalue decomposition of the image deconvolution matrix and the other based on the prior knowledge of the "dirty image" using the available data. The methods are evaluated by simulations as well as actual data from a phased-array radio telescope in the Netherlands, the Low Frequency Array Radio Telescope (LOFAR).

AB - Image formation using the data from an array of sensors is a familiar problem in many fields such as radio astronomy, biomedical and geodetic imaging. The problem can be formulated as a least squares (LS) estimation problem and becomes ill-posed at high resolutions, i.e. large number of image pixels. In this paper we propose two regularization methods, one based on weighted truncation of the eigenvalue decomposition of the image deconvolution matrix and the other based on the prior knowledge of the "dirty image" using the available data. The methods are evaluated by simulations as well as actual data from a phased-array radio telescope in the Netherlands, the Low Frequency Array Radio Telescope (LOFAR).

KW - radio astronomy

KW - Array signal processing

KW - image formation

KW - interferometry

KW - regularization

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DO - 10.1109/icassp.2016.7472291

M3 - Conference contribution

SP - 3316

EP - 3320

BT - 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

A2 - Dong, Min

A2 - Zheng, Thomas Fang

PB - IEEE

CY - Danvers, MA

T2 - 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016

Y2 - 20 March 2016 through 25 March 2016

ER -

Radioastronomical image reconstruction with regularized least squares

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Bibliographical note

Keywords

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