AN ITERATIVE SHRINKAGE DECONVOLUTION FOR ANGULAR SUPERRESOLUTION IMAGING IN FORWARD-LOOKING SCANNING RADAR

The aim of angular super-resolution is to surpass the real-beam resolution. In this paper, a method for forward-looking scanning radar angular super-resolution imaging through a deconvolution method is proposed, which incorporates the prior information of the target’s scattering characteristics. We first mathematically formulate the angular super-resolution problem of forward-looking scanning radar as a maximum a posteriori (MAP) estimation task based on the forward model, and convert it to an equivalent unconstrained optimization problem by applying the log-transforms to the posterior probability, which guarantees the solution converges to a global optimum of an associated MAP problem and it is easy to implement. We then implement the unconstrained optimization task in convex optimization framework using an iterative shrinkage method, and the computational complexity of the proposed algorithm is also discussed. Since the anti log-likelihood of the noise distribution and the prior knowledge of the scene are utilized, the proposed method is able to achieve angular super-resolution imaging in forward-looking scanning radar effectively. Numerical simulations and experimental results based on real data are presented to verify that the proposed deconvolution algorithm has better performance in preserving angular super-resolution accuracy and suppressing the noise amplification.