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repetition frequency (PRF). Given the system complexity and resourceconstraints, it is often difficult to achieve high imaging performance and lowambiguity without compromising the swath. In this paper, we propose a jointoptimization framework for sparse strip SAR imaging algorithms and azimuthundersampling patterns based on a deep convolutional neural network, combinedwith matched filter (MF) approximate measurement operators and inverse MFoperators, referred to as MF-JMoDL-Net, for sparse SAR imaging methods.Compared with conventional sparse SAR imaging, MF-JMoDL-Net enables us toalleviate the limitations imposed by PRF. In the proposed scheme, joint andcontinuous optimization of azimuth undersampling patterns and convolutionalneural network parameters are implemented to suppress azimuth ambiguity andenhance sparse SAR imaging quality. Experiments and comparisons under variousconditions demonstrate the effectiveness and superiority of the proposedframework in imaging results.

MF-JMoDL-Net: A Deep Network for Azimuth Undersampling Pattern Design and Ambiguity Suppression for Sparse SAR Imaging