Dimension-reduced radio astronomical imaging based on sparse reconstruction

Shuimei Zhang, Yujie Gu, Chang Hee Won, Yimin D. Zhang

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

9 Scopus citations

Abstract

Modern radio telescopes commonly use antenna arrays to achieve high-resolution imaging, and various beamforming techniques have been developed in radio astronomy to generate dirty images. Because the manifold of a radio telescope array varies over time due to Earth rotation, beamformers are separately designed and implemented at each time epoch, and the resulting images are averaged over multiple epochs to form enhanced dirty images. Because astronomical scenes are typically sparse, we propose a new method through sparse reconstruction to obtain clean astronomical images. To reduce the computational complexity, a singular value decomposition based compressive sensing scheme is applied. The proposed method offers reduced computational complexity while maintaining the high quality of the sparse reconstruction. Unlike traditional beamforming techniques which require an additional deconvolution procedure for clean image formation, the proposed technique provides clean astronomical images directly with accurate estimation of the source position and intensity.

Original languageEnglish
Title of host publication2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop, SAM 2018
PublisherIEEE Computer Society
Pages470-474
Number of pages5
ISBN (Print)9781538647523
DOIs
StatePublished - 27 Aug 2018
Externally publishedYes
Event10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018 - Sheffield, United Kingdom
Duration: 8 Jul 201811 Jul 2018

Publication series

NameProceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
Volume2018-July
ISSN (Electronic)2151-870X

Conference

Conference10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018
Country/TerritoryUnited Kingdom
CitySheffield
Period8/07/1811/07/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Funding

This work is supported in part by the National Science Foundation (NSF) under grant AST-1547420.

FundersFunder number
National Science FoundationAST-1547420

    Keywords

    • Compressive sensing
    • Data fusion
    • Dimension reduction
    • Radio astronomical imaging

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