The Race to Improve Radar Imagery: An overview of recent progress in statistical sparsity-based techniques

2016/11/01 by Lifan Zhao, Lu Wang, Lei Yang +2
Engineering · #Sparse and Compressive Sensing Techniques #Advanced SAR Imaging Techniques #Microwave Imaging and Scattering Analysis

paper · doi:10.1109/msp.2016.2573847

Abstract

The exploitation of sparsity has significantly advanced the field of radar imaging over the last few decades, leading to substantial improvements in the resolution and quality of the processed images. More recent developments in compressed sensing (CS) suggest that statistical sparsity can lead to further performance benefits by imposing sparsity as a statistical prior on the considered signal. In this article, a comprehensive survey is made of recent progress on statistical sparsity based techniques for various radar imagery applications.

Citations

• Atomic Decomposition by Basis Pursuit
• The horseshoe estimator for sparse signals
• An Introduction To Compressive Sampling [A sensing/sampling paradigm that goes against the common knowledge in data acquisition]

Related

• Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing
• On sparsity averaging
• Sparsity averaging for radio-interferometric imaging
• Sparsity-Aware Learning and Compressed Sensing: An Overview
• A Survey on Compressive Sensing: Classical Results and Recent Advancements