MTRC Compensation and Sparse Aperture ISAR Imaging Based on Sparse Bayesian Learning

In inverse synthetic aperture radar (ISAR) imaging, large variation angle contributes to high azimuthal resolution, provided that migration through resolution cell (MTRC) is compensated during the coherent processing interval (CPI). Usually, it is challenging to realize accurate MTRC compensation in the sparse aperture (SA) case, which tends to impede coherent accumulation and degrade the imaging performance. In this paper, a novel high-resolution SA ISAR imaging algorithm based on sparse Bayesian learning (SBL) is proposed, which can effectively incorporate the MTRC compensation and SA imaging simultaneously. In the scheme, the SA imaging is modeled as an inversion process of a linear problem with sparse prior, where chirp-Fourier dictionary is constructed to describe the MTRC during rotational motion. Then, the sparse signal recovery problem is solved using variational Bayesian inference (VBI) algorithm. Experiments on simulated and measured data confirm the effectiveness of the proposal.