Scanning Radar Forward-Looking Superresolution Imaging Based on the Weibull Distribution for a Sea-Surface Target
Yin Zhang, Jiahao Shen, Xingyu Tuo, Haiguang Yang, Yongchao Zhang, Yulin Huang, Jianyu Yang
Abstract
To realize high azimuth resolution for sea-surface targets, this paper proposes a superresolution imaging method that relies on the Weibull distribution. The proposed method introduces the generalized Gaussian distribution and Weibull distribution to represent the statistical distribution function of the target prior information and sea clutter, respectively. The corresponding objective function was derived under the maximum a posteriori (MAP) criterion. To address the nonlinearity of the objective function, this paper adopts the NewtonRaphson iterative method to resolve it. Simulations and experimental data assessment indicate that the proposed method has superior superresolution imaging performance compared with other traditional superresolution methods for sea-surface target imaging.