Bayesian Inverse Synthetic Aperture Radar Imaging by Exploiting Sparse Probing Frequencies

This letter proposes a Bayesian compressed sensing (BCS) method for high-resolution inverse synthetic aperture radar (ISAR) imaging when the basis matrix depends on an unknown rotation rate. In the proposed method, the radar transmits only a few probing frequencies instead of a wideband signal. In doing so, both the rotation rate and a high-resolution ISAR image can be simultaneously retrieved by jointly applying the BCS algorithm using Gaussian prior and a gradient-based search algorithm to exploit the sparse probing frequencies. The effectiveness of the proposed method is verified by experimental results with both simulated data and real data.