Sparse representation for the ISAR image reconstruction

Mengqi Hu; John Montalbo; Shuxia Li; Ligang Sun; Zhijun G. Qiao

doi:10.1117/12.2228095

Sparse representation for the ISAR image reconstruction

Mengqi Hu^*, John Montalbo, Shuxia Li, Ligang Sun, Zhijun G. Qiao

^*Corresponding author for this work

University of Texas Rio Grande Valley

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

In this paper, a sparse representation of the data for an inverse synthetic aperture radar (ISAR) system is provided in two dimensions. The proposed sparse representation motivates the use a of a Convex Optimization that recovers the image with far less samples, which is required by Nyquist-Shannon sampling theorem to increases the efficiency and decrease the cost of calculation in radar imaging.

Original language	English
Title of host publication	Compressive Sensing V
Subtitle of host publication	From Diverse Modalities to Big Data Analytics
Editors	Fauzia Ahmad
Publisher	SPIE
ISBN (Electronic)	9781510600980
DOIs	https://doi.org/10.1117/12.2228095
Publication status	Published - 2016
Externally published	Yes
Event	Compressive Sensing V: From Diverse Modalities to Big Data Analytics - Baltimore, United States Duration: 20 Apr 2016 → 21 Apr 2016

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	9857
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	Compressive Sensing V: From Diverse Modalities to Big Data Analytics
Country/Territory	United States
City	Baltimore
Period	20/04/16 → 21/04/16

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10.1117/12.2228095

Cite this

@inproceedings{48d08d1daa5147e4b163b78e23689a63,

title = "Sparse representation for the ISAR image reconstruction",

abstract = "In this paper, a sparse representation of the data for an inverse synthetic aperture radar (ISAR) system is provided in two dimensions. The proposed sparse representation motivates the use a of a Convex Optimization that recovers the image with far less samples, which is required by Nyquist-Shannon sampling theorem to increases the efficiency and decrease the cost of calculation in radar imaging.",

author = "Mengqi Hu and John Montalbo and Shuxia Li and Ligang Sun and Qiao, {Zhijun G.}",

note = "Publisher Copyright: {\textcopyright} 2016 SPIE.; Compressive Sensing V: From Diverse Modalities to Big Data Analytics ; Conference date: 20-04-2016 Through 21-04-2016",

year = "2016",

doi = "10.1117/12.2228095",

language = "English",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

publisher = "SPIE",

editor = "Fauzia Ahmad",

booktitle = "Compressive Sensing V",

}

Hu, M, Montalbo, J, Li, S, Sun, L & Qiao, ZG 2016, Sparse representation for the ISAR image reconstruction. in F Ahmad (ed.), Compressive Sensing V: From Diverse Modalities to Big Data Analytics., 98570B, Proceedings of SPIE - The International Society for Optical Engineering, vol. 9857, SPIE, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, Baltimore, United States, 20/04/16. https://doi.org/10.1117/12.2228095

Sparse representation for the ISAR image reconstruction. / Hu, Mengqi; Montalbo, John; Li, Shuxia et al.
Compressive Sensing V: From Diverse Modalities to Big Data Analytics. ed. / Fauzia Ahmad. SPIE, 2016. 98570B (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9857).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Sparse representation for the ISAR image reconstruction

AU - Hu, Mengqi

AU - Montalbo, John

AU - Li, Shuxia

AU - Sun, Ligang

AU - Qiao, Zhijun G.

PY - 2016

Y1 - 2016

N2 - In this paper, a sparse representation of the data for an inverse synthetic aperture radar (ISAR) system is provided in two dimensions. The proposed sparse representation motivates the use a of a Convex Optimization that recovers the image with far less samples, which is required by Nyquist-Shannon sampling theorem to increases the efficiency and decrease the cost of calculation in radar imaging.

AB - In this paper, a sparse representation of the data for an inverse synthetic aperture radar (ISAR) system is provided in two dimensions. The proposed sparse representation motivates the use a of a Convex Optimization that recovers the image with far less samples, which is required by Nyquist-Shannon sampling theorem to increases the efficiency and decrease the cost of calculation in radar imaging.

UR - http://www.scopus.com/inward/record.url?scp=84978743166&partnerID=8YFLogxK

U2 - 10.1117/12.2228095

DO - 10.1117/12.2228095

M3 - Conference Proceeding

AN - SCOPUS:84978743166

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Compressive Sensing V

A2 - Ahmad, Fauzia

PB - SPIE

T2 - Compressive Sensing V: From Diverse Modalities to Big Data Analytics

Y2 - 20 April 2016 through 21 April 2016

ER -

Sparse representation for the ISAR image reconstruction

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