Sparse Bayesian Learning Based Three-Dimensional Imaging for Antenna Array Radar

In recent years, the development of compressed sensing and sparse representation provide us with a broader perspective of three-dimensional (3-D) imaging. In this work, we propose a 3-D imaging method based on a sparse Bayesian learning(SBL) framework for antenna array radar. It solves the problem of long-term accumulation and complicated motion compensation problem that occurs with interferometric inverse synthetic aperture radar (InISAR). Using the framework, the proposed method can automatically learn optimal hyper-parameters from the data at a low computational cost. Experimental results show that the proposed method has advantages in terms of 3-D imaging accuracy and computational efficiency compared to existing methods.