Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing
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Willsky Cetin_spm-jul-2014_final_submitted.pdf
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Author(s)
Cetin, Mujdat
Stojanovic, Ivana
Onhon, Ozben
Varshney, Kush
Samadi, Sadegh
Karl, William Clem
Willsky, Alan S.
Date Issued
June 2014
Journal
IEEE Signal Processing Magazine
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Cetin, Mujdat, Ivana Stojanovic, Ozben Onhon, Kush Varshney, Sadegh Samadi, William Clem Karl, and Alan S. Willsky. “Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, Autofocusing, Moving Targets, and Compressed Sensing.” IEEE Signal Processing Magazine 31, no. 4 (July 2014): 27–40.
Version
Author's final manuscript
Abstract
This article presents a survey of recent research on sparsity-driven synthetic aperture radar (SAR) imaging. In particular, it reviews 1) the analysis and synthesis-based sparse signal representation formulations for SAR image formation together with the associated imaging results, 2) sparsity-based methods for wide-angle SAR imaging and anisotropy characterization, 3) sparsity-based methods for joint imaging and autofocusing from data with phase errors, 4) techniques for exploiting sparsity for SAR imaging of scenes containing moving objects, and 5) recent work on compressed sensing (CS)-based analysis and design of SAR sensing missions.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/msp.2014.2312834
