Joint sparsity-driven inversion and model error correction for SAR imaging

Önhon, Özben Naime (2012) Joint sparsity-driven inversion and model error correction for SAR imaging. [Thesis]

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Image formation algorithms in a variety of applications have explicit or implicit dependence on a mathematical model of the observation process. Inaccuracies in the observation model may cause various degradations and artifacts in the reconstructed images. The application of interest in this thesis is synthetic aperture radar (SAR) imaging, which particularly suffers from motion-induced model errors. These types of errors result in phase errors in SAR data which cause defocusing of the reconstructed images. Particularly focusing on imaging of fields that admit a sparse representation, we propose a sparsity-driven method for joint SAR imaging and phase error correction. In this technique, phase error correction is performed during the image formation process. The problem is set up as an optimization problem in a nonquadratic regularization-based framework. The method involves an iterative algorithm each iteration of which consists of consecutive steps of image formation and model error correction. Experimental results show the effectiveness of the proposed method for various types of phase errors, as well as the improvements it provides over existing techniques for model error compensation in SAR.
Item Type: Thesis
Uncontrolled Keywords: Synthetic aperture radar. -- Regularization-based imaging. -- Sparsity. -- Model errors. -- Phase errors. -- Autofocus. -- Sentetik açıklıklı radar. -- Sentetik açıklık radarı. -- Düzenlileştirmeye dayalı görüntü oluşturma. -- Seyreklik. -- Model hataları. -- Faz hataları. -- Otomatik odaklama.
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Electronics
Faculty of Engineering and Natural Sciences
Depositing User: IC-Cataloging
Date Deposited: 06 Jul 2014 12:49
Last Modified: 26 Apr 2022 10:01

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