A method for radar coincidence imaging with model errors

Abstract

Radar coincidence imaging (RCI) is a novel staring imaging technique which was originated from the optical coincidence imaging. In RCI, the reference matrix needs to be computed precisely to reconstruct the image. However, the reference matrix is difficult to know exactly as model errors exist in most applications. In this paper, RCI model with model errors is derived. Based on Bayesian framework, an algorithm called Regularization-FOCal Underdetermined System Solver (R-FOCUSS) method is proposed to solve the RCI problem with model errors. The scattering coefficients and the perturbation matrix can be calculated during the iterations, so the image can be reconstructed. Results of numerical experiments demonstrate that the algorithm can achieve outstanding imaging performance.