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28 April 2009 Enhancement of multi-pass 3D circular SAR images using sparse reconstruction techniques
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This paper demonstrates image enhancement for wide-angle, multi-pass three-dimensional SAR applications. Without sufficient regularization, three-dimensional sparse-aperture imaging from realistic data-collection scenarios results in poor quality, low-resolution images. Sparsity-based image enhancement techniques may be used to resolve high-amplitude features in limited aspects of multi-pass imagery. Fusion of the enhanced images across multiple aspects in an approximate GLRT scheme results in a more informative view of the target. In this paper, we apply two sparse reconstruction techniques to measured data of a calibration top-hat and of a civilian vehicle observed in the AFRL publicly-released 2006 Circular SAR data set. First, we employ prominent-point autofocus in order to compensate for unknown platform motion and phase errors across multiple radar passes. Each sub-aperture of the autofocused phase history is digitally-spotlighted (spatially low-pass filtered) to eliminate contributions to the data due to features outside the region of interest, and then imaged with l1-regularized least squares and CoSaMP. The resulting sparse sub-aperture images are non-coherently combined to obtain a wide-angle, enhanced view of the target.
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Matthew Ferrara, Julie Ann Jackson, and Christian Austin "Enhancement of multi-pass 3D circular SAR images using sparse reconstruction techniques", Proc. SPIE 7337, Algorithms for Synthetic Aperture Radar Imagery XVI, 733702 (28 April 2009);


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