Abstract
Recently, Compressive Sensing has become a highly attractive technique for SAR imaging since it outperforms existing methods. In this paper, a Sparse Bayesian Learning based approach in Compressive Sensing framework is proposed for SAR sensor network imaging to reduce the required number of sensors and to obtain super-resolution in the elevation direction. Specifically, the Trench-Zohar inversion is also adapted to the normal Sparse Bayesian Learning algorithm to reduce the computation time and storage requirements. The advanced efficiency of the proposed approach is validated by results achieved from different simulations.
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