  For 3D Synthetic Aperture Radar (SAR) imaging, one typical approach is to
achieve the cross-track 1D focusing for each range-azimuth pixel after
obtaining a stack of 2D complex-valued images. The cross-track focusing is the
main difficulty as its aperture length is limited and the antenna positions are
usually non-uniformly distributed. Sparsity regularization methods are widely
used to tackle these problems. However, these methods are of obvious
limitations. The most well-known ones are their heavy computational burdens and
unsatisfied stabilities. In this letter, an efficient deep network-based
cross-track imaging method is proposed. When trained, the imaging process, i.e.
the forward propagation of the network, is made up of simple matrix-vector
calculations and element-wise nonlinearity operations, which significantly
speed up the imaging. Also, we find that the deep network is of good robustness
against noise and model errors. Comprehensive simulations and experiments have
been carried out, and the superiority of the proposed method can be clearly
seen.
