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13 June 2014 Model-based 3D SAR reconstruction
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Abstract
Three dimensional scene reconstruction with synthetic aperture radar (SAR) is desirable for target recognition and improved scene interpretability. The vertical aperture, which is critical to reconstruct 3D SAR scenes, is almost always sparsely sampled due to practical limitations, which creates an underdetermined problem. This papers explores 3D scene reconstruction using a convex model-based approach. The approach developed is demonstrated on 3D scenes, but can be extended to SAR reconstruction of sparsely sampled signals in the spatial and, or, frequency domains. The model-based approach enables knowledge-aided image formation (KAIF) by incorporating spatial, aspect, and sparsity magnitude terms into the image reconstruction. The incorporation of these terms, which are based on prior scene knowledge, will demonstrate improved results compared to traditional image formation algorithms. The SAR image formation problem is formulated as a second order cone program (SOCP) and the results are demonstrated on 3D scenes using simulated data and data from the GOTCHA data collect.1 The model-based results are contrasted against traditional backprojected images.
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Chad Knight, Jake Gunther, and Todd Moon "Model-based 3D SAR reconstruction", Proc. SPIE 9093, Algorithms for Synthetic Aperture Radar Imagery XXI, 909308 (13 June 2014); https://doi.org/10.1117/12.2050832
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