Improved radio astronomical imaging based on sparse reconstruction

Shuimei Zhang, Yujie Gu, William C. Barott, Yimin D. Zhang

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

3 Scopus citations


Modern radio telescopes commonly use antenna arrays, and high-resolution imaging techniques exploiting radio astronomical signals collected at these antenna arrays play a critical role to achieve their missions. Beamforming techniques have been developed in radio astronomy to generate dirty images with limited image resolutions for many years. Because the manifold of a radio telescope array varies over time due to the Earth rotation, beamformers are separately designed and implemented at each time epoch, and the resulting images are averaged to form enhanced dirty images. Considering the fact that astronomical scenes are typically sparse, we present a new method through sparse reconstruction to obtain clean astronomical images. Sparse reconstruction methods that fuse the measured data observed at multiple time epochs are examined and compared. Unlike beamforming techniques which require an additional deconvolution procedure for clean image formation, the proposed technique provides clean astronomical images with accurate estimation of the source position and a high dynamic range.

Original languageEnglish
Title of host publicationCompressive Sensing VII
Subtitle of host publicationFrom Diverse Modalities to Big Data Analytics
EditorsFauzia Ahmad
ISBN (Electronic)9781510618275
StatePublished - 2018
Externally publishedYes
EventCompressive Sensing VII: From Diverse Modalities to Big Data Analytics 2018 - Orlando, United States
Duration: 17 Apr 201819 Apr 2018

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceCompressive Sensing VII: From Diverse Modalities to Big Data Analytics 2018
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2018 SPIE.


  • Compressive sensing
  • data fusion
  • image formation
  • interferometry
  • radio astronomy


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