An improved angular super-resolution approach based on constrained optimization

The problem of angular super-resolution has attracted significant attention over the last decades. The difficultly and keystone of implementation of the angular super-resolution however, lies in how to cope with the ill-posed nature of these problems. Independently, there has been a surge in algorithms for angular super-resolution in optical and microwave imaging field. These algorithms are supported on some form of a prior knowledge about the original image to be estimated. In this paper, a novel super-resolution approach based on constrained optimization is proposed. Using this approach, the angular super-resolution problem is transformed into constrained optimization problem. The angular super-resolution problem can be solved by searching the global optimal solution to the constrained optimization problem. The advantage of this approach is that it is not only able to accomplish the super-resolution task, but also to reduces the loss of underlying field reflectivity magnitude that can not be solved by traditional approaches. The simulations given at the end of this paper verify the effectiveness of this angular super-resolution approach.