SAR imaging from randomly sampled phase history using compressive sensing

Amit Kumar Mishra*, Rohan Phogat, Shikhar Mann

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

Abstract

Reconstructing synthetic aperture Radar (SAR) images from gapped phase history or k space data, is a major problem for SAR engineers. In this work we use the newly proposed compressive sensing (CS) algorithms to form SAR images of randomly and sparsely sampled k space data. We also investigate the effect of adding phase noise of various degrees of severity in the sparse and random k space data. We show that CS based algorithms can intelligibly reconstruct SAR images from randomly sparse phase history data and can tolerate a good amount of phase noise corruption. Dantzig selector based CS algorithm was found to perform better than the usual l 1 norm based CS algorithm.

Original languageEnglish
Title of host publication2012 13th International Radar Symposium, IRS-2012
PublisherIEEE Press
Pages221-224
Number of pages4
ISBN (Electronic)978-1-4577-1837-3
ISBN (Print)978-1-4577-1838-0
DOIs
Publication statusPublished - 23 May 2012
Externally publishedYes
Event2012 13th International Radar Symposium, IRS-2012 - Warsaw, Poland
Duration: 23 May 201225 May 2012

Publication series

NameProceedings International Radar Symposium
ISSN (Print)2155-5753

Conference

Conference2012 13th International Radar Symposium, IRS-2012
Country/TerritoryPoland
CityWarsaw
Period23 May 201225 May 2012

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