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For 3D Synthetic Aperture Radar (SAR) imaging, one typical approach is toachieve the cross-track 1D focusing for each range-azimuth pixel afterobtaining a stack of 2D complex-valued images. The cross-track focusing is themain difficulty as its aperture length is limited and the antenna positions areusually non-uniformly distributed. Sparsity regularization methods are widelyused to tackle these problems. However, these methods are of obviouslimitations. The most well-known ones are their heavy computational burdens andunsatisfied stabilities. In this letter, an efficient deep network-basedcross-track imaging method is proposed. When trained, the imaging process, i.e.the forward propagation of the network, is made up of simple matrix-vectorcalculations and element-wise nonlinearity operations, which significantlyspeed up the imaging. Also, we find that the deep network is of good robustnessagainst noise and model errors. Comprehensive simulations and experiments havebeen carried out, and the superiority of the proposed method can be clearlyseen.

Fast Super-resolution 3D SAR Imaging Using an Unfolded Deep Network