【作者】 王晓宇； 朱莉； 吴宇飞；

【Author】 WANG Xiaoyu;ZHU Li;WU Yufei;School of Electronic and Optical Engineering,Nanjing University of Science&Technology;

【机构】 南京理工大学电子工程与光电技术学院；

【摘要】 针对三维SAR成像中单正则化稀疏模型难以保留目标空间非稀疏结构特征的问题,本文提出基于复合先验约束的SAR成像方法。通过构建低秩稀疏联合优化的目标函数,采用截断加权核范数表征目标背景的低秩特性,克服传统核范数对奇异值不平衡惩罚的缺陷,同时引入对数稀疏惩罚LSP约束目标散射点的稀疏特性。该方法通过交替方向乘子法将复合优化问题分解为全局变量、低秩分量、稀疏分量三个子问题进行迭代求解,其中截断加权核范数通过自适应权重分配机制保留主奇异值表征的结构信息,LSP近端算子则通过动态阈值收缩实现稀疏特征的精确重构。仿真和实测数据实验表明,本文方法有效改善了有限观测条件下目标结构特征保持能力与成像质量。更多还原

【Abstract】 Aiming at the problem that single-regularized sparse models in 3D SAR imaging struggle to preserve spatial non-sparse structural features of targets,this paper proposes a SAR imaging method based on composite prior constraints.By constructing a low-rank and sparse joint optimization objective function,the truncated weighted nuclear norm is adopted to characterize the low-rank property of the target background,overcoming the defect of imbalanced penalty on singular values in traditional nuclear norms.Meanwhile,the logarithmic sparse penalty(LSP) is introduced to constrain the sparse property of target scattering points.This method decomposes the composite optimization problem into three subproblems:global variables,low-rank components,and sparse components,for iterative solving via the alternating direction method of multipliers(ADMM).Specifically,the truncated weighted nuclear norm retains structural information represented by dominant singular values through an adaptive weight allocation mechanism,while the LSP proximal operator achieves precise sparse feature reconstruction via dynamic threshold shrinkage.The simulation and measured data experiments demonstrate that the proposed method effectively enhances the preservation capability of target structural features and improves imaging quality under limited observation conditions.更多还原