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Progress In Electromagnetics Research
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SPARSE AUTOFOCUS RECOVERY FOR UNDER-SAMPLED LINEAR ARRAY SAR 3-D IMAGING

By S.-J. Wei and X.-L. Zhang

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Abstract:
Linear array synthetic aperture radar (LASAR) is a promising radar 3-D imaging technique. In this paper, we address the problem of sparse recovery of LASAR image from under-sampled and phase errors interrupted echo data. It is shown that the unknown LASAR image and the nuisance phase errors can be constructed as a bilinear measurement model, and then the under-sampled LASAR imaging with phase errors can be mathematically transferred into sparse signal recovery by solving an ill-conditioned constant modulus linear program (ICCMLP) problem. Exploiting the prior sparse spatial feature of the observed targets, a new super-resolution sparse autofocus recovery algorithm is proposed for under-sampled LASAR 3-D imaging. The algorithm is an iterative minimize estimation procedure, wherein it converts the ICCMLP into two independent convex optimal problems, and joints l1-norm reweights least square regularization and semi-definite relax to find the optimal solutions. Simulated and experimental results confirm that the proposed method outperforms the classical autofocus techniques in under-sampled LASAR imaging.

Citation:
S.-J. Wei and X.-L. Zhang, "Sparse Autofocus Recovery for Under-Sampled Linear Array SAR 3-D Imaging," Progress In Electromagnetics Research, Vol. 140, 43-62, 2013.
doi:10.2528/PIER13020614
http://www.jpier.org/PIER/pier.php?paper=13020614

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