A Split SPICE-TV Super-Resolution Method for Scanning Radar

Recently, the sparse iterative covariance fitting estimation (SPICE) method has garnered attention for scanning radar super-resolution imaging. While it offers improved azimuth resolution, a notable drawback is its limited efficacy in reconstructing target contours. In this paper, a Split SPICE total variation (Split SPICE-TV) super-resolution method is proposed to address this problem. First, the scanning radar angular super-resolution problem is transformed into a convex optimization problem, leveraging the sparse covariance fitting criterion alongside TV regularization constraint. Then, the split Bregman method is employed to derive a efficient close-form solution for this problem. Furthermore, the proposed Split SPICE-TV method effectively mitigates the computational complexity of traditional SPICE-TV based on CVX and furnishes a closed-form solution, which is beneficial for practical applications. Simulation results validate the superiority of the proposed method.