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Proceedings Paper

Superresolution inverse synthetic aperture radar (ISAR) imaging using compressive sampling
Author(s): Suman K. Gunnala; Saibun Tjuatja
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Paper Abstract

A method based on compressive sampling to achieve superresolution in ISAR imaging is presented. The superresolution ISAR imaging algorithm is implemented by enforcing the sparsity constraints via random compressive sampling of the measured data. Sparsity constraint ratio (SCR) is used as a design parameter. Mutual coherence is used as a quantitative measure to determine the optimal SCR. ISAR data for full angular sector as well as different partial angular sectors are utilized in this study. Results show that significant resolution enhancement is achieved around optimal SCR of 0.2.

Paper Details

Date Published: 19 April 2010
PDF: 10 pages
Proc. SPIE 7699, Algorithms for Synthetic Aperture Radar Imagery XVII, 76990A (19 April 2010); doi: 10.1117/12.850225
Show Author Affiliations
Suman K. Gunnala, The Univ. of Texas at Arlington (United States)
Saibun Tjuatja, The Univ. of Texas at Arlington (United States)


Published in SPIE Proceedings Vol. 7699:
Algorithms for Synthetic Aperture Radar Imagery XVII
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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