Fast Bayesian Method for Joint Sparse ISAR Imaging and Motion Compensation for Uniform Rotating Targets

For inverse synthetic aperture radar (ISAR) imaging under sparse aperture (SA) conditions, the rotation motion compensation is seldom considered. However, with the improvement of resolution, the migration through resolution cell (MTRC) cannot be ignored. Traditional methods for rotation motion compensation generally fail in SA cases. This article proposes a method to jointly implement sparse imaging and compensation of the MTRC in a structured sparse Bayesian learning (SBL) framework. Due to the coupling of fast time and slow time, the observation model is established in a vectorized form. To reduce the computational complexity, approximated inference methods are utilized to achieve fast inference for the posteriors. Maximum contrast (MC) criterion is adopted to estimate the rotation parameters. The approximated implementation for the forward operator and backward operator is discussed to further accelerate the algorithm. Experimental results based on simulated and measured data validate the effectiveness and efficiency of the proposed methods.