Joint Sparsity-Driven Inversion and Model Error Correction for SAR Imaging (2012)
Abstract / truncated to 115 words
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 ... toggle 6 keywordssynthetic aperture radar – regularization-based imaging – sparsity – model errors – phase errors – autofocus
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