Balanced Tikhonov and Total Variation Deconvolution Approach for Radar Forward-Looking Super-Resolution Imaging
Weibo Huo, Xingyu Tuo, Yin Zhang, Yongchao Zhang, Yulin Huang
Abstract
In radar forward-looking super-resolution imaging, improving the azimuth resolution while acquiring the contour information of the target has significant research value. In this letter, an approach based on the balanced Tikhonov and total variation (TV) deconvolution is proposed for radar forward-looking super-resolution imaging. We combine the Tikhonov regularization and TV regularization to construct the objective function and resolve the respective cost function using the alternating direction method of multipliers (ADMM). In each iteration, the gradient function of the target scattering coefficient is used as the adaptive weighted parameter to control automatically the weighting between the penalty terms from TV and the Tikhonov regularization. For the target with a sharper outline, the proportion of TV regularization penalty terms is increased; for the target with a smoother outline, the proportion of penalty term from the Tikhonov regularization is enhanced. The simulation and experimental results are considered to show the effectiveness of the proposed method. Compared with traditional super-resolution imaging methods, the proposed approach has superior outline retention capacity.