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

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

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 f...

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Main Authors: Zhao, Lifan, Wang, Lu, Yang, Lei, Zoubir, Abdelhak M., Bi, Guoan
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2016
Subjects:
Statistical analysis
Image quality
Online Access:https://hdl.handle.net/10356/84674
http://hdl.handle.net/10220/41933
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-846742020-03-07T13:57:28Z The Race to Improve Radar Imagery: An overview of recent progress in statistical sparsity-based techniques Zhao, Lifan Wang, Lu Yang, Lei Zoubir, Abdelhak M. Bi, Guoan School of Electrical and Electronic Engineering Statistical analysis Image quality 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. MOE (Min. of Education, S’pore) Accepted version 2016-12-22T07:43:55Z 2019-12-06T15:49:17Z 2016-12-22T07:43:55Z 2019-12-06T15:49:17Z 2016 Journal Article Zhao, L., Wang, L., Yang, L., Zoubir, A. M., & Bi, G. (2016). The Race to Improve Radar Imagery: An overview of recent progress in statistical sparsity-based techniques. IEEE Signal Processing Magazine, 33(6), 85-102. 1053-5888 https://hdl.handle.net/10356/84674 http://hdl.handle.net/10220/41933 10.1109/MSP.2016.2573847 en IEEE Signal Processing Magazine © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/MSP.2016.2573847]. 35 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Statistical analysis
Image quality
spellingShingle Statistical analysis
Image quality
Zhao, Lifan
Wang, Lu
Yang, Lei
Zoubir, Abdelhak M.
Bi, Guoan
The Race to Improve Radar Imagery: An overview of recent progress in statistical sparsity-based techniques
description 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.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Zhao, Lifan
Wang, Lu
Yang, Lei
Zoubir, Abdelhak M.
Bi, Guoan
format Article
author Zhao, Lifan
Wang, Lu
Yang, Lei
Zoubir, Abdelhak M.
Bi, Guoan
author_sort Zhao, Lifan
title The Race to Improve Radar Imagery: An overview of recent progress in statistical sparsity-based techniques
title_short The Race to Improve Radar Imagery: An overview of recent progress in statistical sparsity-based techniques
title_full The Race to Improve Radar Imagery: An overview of recent progress in statistical sparsity-based techniques
title_fullStr The Race to Improve Radar Imagery: An overview of recent progress in statistical sparsity-based techniques
title_full_unstemmed The Race to Improve Radar Imagery: An overview of recent progress in statistical sparsity-based techniques
title_sort race to improve radar imagery: an overview of recent progress in statistical sparsity-based techniques
publishDate 2016
url https://hdl.handle.net/10356/84674
http://hdl.handle.net/10220/41933
_version_ 1681042032367239168

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