High-resolution Imaging of Passive Radar Based on Sparse Bayesian Learning

This paper presents a high-resolution imaging method based on Sparse Bayesian Learning (SBL) for passive radar compressed sensing imaging. Under the one-snapshot echo model, the proposed method firstly takes account of the frequency-dependent statistics of the target scattering centers, and changes passive radar imaging into a joint Multiple Measurement Vector (MMV) sparse optimization problem. Further, a hierarchical Bayesian framework for sparsity-inducing priori of the target is established, then the MMV problem is efficiently solved by utilizing the SBL theory. Unlike the previous sparse recovery algorithms relying on the deterministic assumption of the target, the proposed method makes a better use of the target prior information, and has the advantages of adaptively estimating parameters (including the parameters in the priori model of the target, and the unknown noise power) as well as the high-resolution imaging, etc.. Simulation results show the effectiveness of the proposed method.