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Proceedings Paper

Sparse representation for the ISAR image reconstruction
Author(s): Mengqi Hu; John Montalbo; Shuxia Li; Ligang Sun; Zhijun G. Qiao
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Paper 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.

Paper Details

Date Published: 4 May 2016
PDF: 9 pages
Proc. SPIE 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, 98570B (4 May 2016); doi: 10.1117/12.2228095
Show Author Affiliations
Mengqi Hu, Univ. of Texas-Rio Grande Valley (United States)
John Montalbo, Univ. of Texas-Rio Grande Valley (United States)
Shuxia Li, Univ. of Texas-Rio Grande Valley (United States)
Ligang Sun, Univ. of Texas-Rio Grande Valley (United States)
Zhijun G. Qiao, Univ. of Texas-Rio Grande Valley (United States)


Published in SPIE Proceedings Vol. 9857:
Compressive Sensing V: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)

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