Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing
Author(s)Cetin, Mujdat; Stojanovic, Ivana; Onhon, Ozben; Varshney, Kush; Samadi, Sadegh; Karl, William Clem; Willsky, Alan S.; ... Show more Show less
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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.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
IEEE Signal Processing Magazine
Institute of Electrical and Electronics Engineers (IEEE)
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.
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