Atomic Norm Minimization Based Fast Off-Grid Tomographic SAR Imaging With Nonuniform Sampling

The accuracy of the traditional compressed sensing (CS) based tomographic synthetic aperture radar (TomoSAR) imaging is limited by the inappropriate grid partitioning. The atomic norm based processing effectively solves this problem by implementing variable estimation in the continuous domain, that is, avoiding the undesired grid partitioning manipulation. Nevertheless, the performance of the atomic norm based TomoSAR imaging is limited in two main aspects: limited geometry adaptability caused by the uniform sampling requirement and the high computational load. In this paper, a novel atomic norm minimization (ANM) based off-grid TomoSAR imaging is proposed for the fast processing with nonuniform sampling. The main technical contributions are twofold: First, the nonuniformly sampled data is resampled to be uniform where a new geometrical projection-based interpolation is used; Second, the ANM problem is solved by using the non-symmetric cone model to speed up the processing, reducing the computational load from O ( N ² ) to O ( N ). The proposed approaches have been verified by the computer simulations and the real data experiments.