Angular Superresolution for Scanning Radar With Improved Regularized Iterative Adaptive Approach

Abstract

In this letter, an improved regularized iterative adaptive approach (IAA) is proposed for scanning radar angular superresolution. Because the IAA requires matrix inversion, the increasing condition number of the covariance matrix leads to the ill-posed problem of the IAA. Based on this reality, the diagonal loading method is introduced to solve the ill-posed problem. Because the loading value controls the tradeoff between the azimuth resolution and noise amplification, we use the radiometer uncertainty principle to determine the optimum loading value. When compared with the existing angular superresolution approaches, the proposed regularized IAA is shown to provide significant resolution improvement. Numerical results illustrate the superior performance of the proposed regularized IAA.