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