Maximum a posteriori estimation for radar angular super-resolution

Angular super-resolution performance is the key problem in the field of radar imaging. In this paper, we propose an approach to radar angular super-resolution through deconvolution, which is able to increase the resolution of radar image beyond the limitation of system parameters. It relies on the Bayesian formulation approach that enables to incorporate the prior information about the system and the statistical characteristics of scene. We first formulate the radar angular superresolution problem as an linear inverse problem and then convert it to a maximum a posterior (MAP) task using Bayesian theory. We then solve the MAP problem in a convex optimization framework using a shrinkage based iterative procedure, leading to algorithm that guarantees the solution to converge the global maximizer of an associated MAP criterion. Numerical experiments with synthetic data demonstrate the performance of proposed angular super-resolution algorithm.