Real-beam scanning radar angular super-resolution via sparse deconvolution

Radar image resolution is a controlling factor in the radar imaging application. In this paper, we propose an approach to radar angular super-resolution through sparse deconvolution, which is able to increase the resolution of radar image beyond the limitation of system parameters. It relies on the optimization approach that enables to incorporate the prior information about the system and the statistical characteristics of scene. We first formulate the radar angular super-resolution problem as a constrained optimization problem and then convert it to an equivalent unconstrained optimization task using augmented Lagrangian method. We then solve the unconstrained optimization problem in the convex optimization framework using iterative method. Numerical experiments with real data demonstrate that the validity of the proposed method.