An alternating direction method for angular super-resolution in scanning radar

Abstract

Angular super-resolution imaging plays a significant role in the area of the scanning radar imaging. Some deconvolution methods are used to realize the angular super-resolution on scanning radar. However, the ill-posed nature of the deconvolution problem means difficulties and inaccuracies in the search for the solution. In this paper, we present a novel method for angular super-resolution imaging in scanning radar using the alternating direction method for solving the constrained optimization problem. To this end, we first formulate the angular super-resolution problem as deconvolution task and then convert it to a constrained optimization problem by incorporating the prior information of the targets. We then attack the constrained optimization problem in augmented Lagrangian framework using an alternating direction method, leading to the algorithm that can be implemented easily. It is shown in a serious simulation that the proposed algorithm outperforms a number of existing deconvolution algorithms in terms of stability and precision.