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Proceedings Paper

A nonquadratic regularization-based technique for joint SAR imaging and model error correction
Author(s): N. Özben Önhon; Müjdat Çetin
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Paper Abstract

Regularization based image reconstruction algorithms have successfully been applied to the synthetic aperture radar (SAR) imaging problem. Such algorithms assume that the mathematical model of the imaging system is perfectly known. However, in practice, it is very common to encounter various types of model errors. One predominant example is phase errors which appear either due to inexact measurement of the location of the SAR sensing platform, or due to effects of propagation through atmospheric turbulence. We propose a nonquadratic regularization-based framework for joint image formation and model error correction. This framework leads to an iterative algorithm, which cycles through steps of image formation and model parameter estimation. This approach offers advantages over autofocus techniques that involve post-processing of a conventionally formed image. We present results on synthetic scenes, as well as the Air Force Research Labarotory (AFRL) Backhoe data set, demonstrating the effectiveness of the proposed approach.

Paper Details

Date Published: 29 April 2009
PDF: 10 pages
Proc. SPIE 7337, Algorithms for Synthetic Aperture Radar Imagery XVI, 73370C (29 April 2009); doi: 10.1117/12.819842
Show Author Affiliations
N. Özben Önhon, Sabanci Univ. (Turkey)
Müjdat Çetin, Sabanci Univ. (Turkey)


Published in SPIE Proceedings Vol. 7337:
Algorithms for Synthetic Aperture Radar Imagery XVI
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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