Sparse Bayesian Representation of the ISAR Imaging Method Based on ExCoV

By taking into account of the prior information of the sparse signal and the additive noise encountered in the measurement process, the sparse recover algorithm under the Bayesian framework can reconstruct the coefficient better. However, the traditional Sparse Bayesian Learning (SBL) algorithm holds many parameters and its timeliness is poor. In this paper, a new sparse Bayesian learning algorithm named Expansion-Compression Variance- component based method (ExCoV) is considered, which only endows a different variance-component to the significant signal elements. Unlikely, the SBL has a distinct variance component on the all signal elements. In addition, the ExCoV has much less parameters than the SBL. Combined with the Compress Sensing (CS) theory, the ExCoV is used in the ISAR imaging model under the Computerized Tomography (CT) frame, and its applicability and the imaging quality are compared with the Polar Format Algorithm (PFA), Convolution Back Projection Algorithm (CBPA) and the traditional sparse recover algorithm. The point scatter simulation verifies that the Inverse SAR (ISAR) image obtained by the ExCoV has low sidelobe and high resolution, and is not sensitive to noise. The imaging results of real data show that the ExCoV has more sparse ISAR image, indicating that it is a more effective and potential ISAR imaging algorithm.