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

IEEE Transactions on Geoscience and Remote Sensing(2024)

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The accuracy of the traditional compressed sensing (CS) based tomographic synthetic aperture radar (TomoSAR) imaging is limited by 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 article, a novel atomic norm minimization (ANM) based off-grid TomoSAR imaging is proposed for 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 nonsymmetric 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 computer simulations and real data experiments.
Fast off-grid approach,geometrical projection interpolation,nonsymmetric conic model,nonuniform sampling,TomoSAR imaging
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