Abstract
Autofocus is one of the key problems in inverse synthetic aperture radar (ISAR) since the noncooperation of the target motion. For sparse aperture ISAR, classical autofocus algorithms are not suitable due to the discontinuity of the azimuth sampling. In this paper, a novel framework is proposed for ISAR autofocus with sparse aperture. The autofocus problem is transformed into an optimization problem with l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> norm and nonlocal total variation (NLTV) regularization constraints. Therefore, both spatial sparsity and structural information of the target can be considered in the process. Dual iterative computation which combines regularization method and conjugate gradient (CG) algorithm is applied to reconstruct the image and correct the phase error. Results of real data experiments show the effectiveness of the proposed method.
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