High resolution of ISAR imaging based on enhanced sparse Bayesian learning

The compressive sensing (CS) has been successfully used in inverse synthetic aperture radar (ISAR) imaging. Since the sparse reconstruction based on l1 norm is sensitive to the regularized factor and makes it inconvenient to be used in practice, the sparse Bayesian learning (SBL) is considered in this situation, which retains a preferable property of the l0 norm and has no user parameter. In this paper, we proposed the ISAR imaging approach based on enhanced sparse Bayesian learning (ESBL), the ESBL determines the signal support by applying the statistical thresholding to accept the active components of the model, which is integrated into the SBL and improving its accuracy and reducing convergence time. The simulation result show that an accurate reconstruction of high-resolution ISAR images can be obtained than most its counterparts in low SNR, and resulting in lower mean square error (MSE).