Superresolution ISAR Imaging Based on Sparse Bayesian Learning

Recently, compressive sensing (CS) has been successfully used in inverse synthetic aperture radar (ISAR) imaging. Since the exact sparse reconstruction, i.e., l₀-norm constraint, is NP hard, l₁-norm relaxation is widely used at the cost of performance degradation in the sparseness of the solution. The performance of existing CS-based ISAR imaging algorithms is sensitive to the regularized factor, which should be adjusted manually. This makes the existing algorithms inconvenient to be used in practice. It is well known that sparse Bayesian learning (SBL) acts as an effective tool in regression and classification, which is closely related to the CS. Furthermore, all the necessary parameters can be estimated using an efficient evidence maximization procedure in SBL, which retains a preferable property of the l₀-norm diversity measure and can give more sparse solution. Motivated by that, a fully automated ISAR imaging algorithm based on SBL is proposed in this paper. Experimental results based on simulated and measured data show that the proposed algorithm keeps a better balance between the computation load and the sparsity of the reconstruction signal than the existing algorithms.