Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing

2014/06/19 by Müjdat Çetin, Mujdat Cetin, Ivana Stojanovic +9 · 3 citations
Engineering · #Sparse and Compressive Sensing Techniques #Advanced SAR Imaging Techniques #Microwave Imaging and Scattering Analysis

paper · doi:10.1109/msp.2014.2312834

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.

Citations

• Compressed sensing

Cited by

• Pattern-Coupled Sparse Bayesian Learning for Inverse Synthetic Aperture Radar Imaging
• Sketching Datasets for Large-Scale Learning (long version)
• Radar Imaging by Sparse Optimization Incorporating MRF Clustering Prior

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