A SPICE-TV Super-resolution Method for Scanning Radar

Recently, the sparse iterative covariance fitting estimation (SPICE) method has been proposed for airborne scanning radar super-resolution imaging, which offers higher azimuth resolution but poor target contour reconstruction ability. In this paper, a SPICE total variation (SPICE-TV) super-resolution method is proposed to solve this problem. First, the scanning radar angular super-resolution problem is transformed into a convex optimization problem that relies on the sparse covariance fitting criterion and the TV regularization constraint. On the one hand, the weighted sparse norm of SPICE is employed to improve azimuth resolution due to its sparsity. On the other hand, the TV norm is introduced to reconstruct the target contour because it can well keep the target edge information. This convex optimization problem is then solved by CVX. The proposed method provides a higher resolution than the traditional TV method. Moreover, it possesses the ability to reconstruct the target contour compared with the traditional SPICE method. The simulation result verifies the superiority of the proposed method.