Robust recovery for aperture synthesis imaging

Liying Wei, Stefan J. Wijnholds, Paul Hurley

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

5 Citations (Scopus)

Abstract

![CDATA[We propose a sparse reconstruction method based on compressed sensing theory for aperture synthesis imaging. Our algorithm directly works on observational data without gridding. We achieve fast convergence by introducing an adaptive tolerance parameter based on the noise level and a thresholding value based on the cumulative sum of the power of the estimated source components. We demonstrate the accuracy in estimating the source positions and intensities in extremely low signal-to-noise (SNR) scenarios in Monte Carlo simulation. We could recover both point sources and extended sources with our algorithm using a Dirac basis from real data.]]
Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE International Conference on Image Processing, 17-20 September 2017, Beijing, China
PublisherIEEE
Pages3570-3574
Number of pages5
ISBN (Print)9781509021758
DOIs
Publication statusPublished - 2017
EventIEEE International Conference on Image Processing -
Duration: 17 Sept 2017 → …

Publication series

Name
ISSN (Print)1522-4880

Conference

ConferenceIEEE International Conference on Image Processing
Period17/09/17 → …

Keywords

  • algorithms
  • antenna arrays
  • interferometry
  • radio astronomy

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