基于属性散射信息的随机梯度最小方差追踪SAR超分辨重建算法

Super-resolution SAR reconstruction algorithm using stochastic gradient minimum variance pursuit with attributed scattering priors

无论军事还是民用合成孔径雷达(SAR)应用领域,对实现目标更高分辨、更精细描述的期望和需求都十分迫切。在稀疏表示框架下,构建了基于属性散射中心模型(ASC)部件级局部散射模型的SAR重建观测模型;提出一种基于信号域的散射中心属性参数空间分类策略,并联合频域外推,提出一种基于随机梯度最小方差追踪的部件级超分辨SAR重建算法。该算法最终的超分辨SAR图像由FFT获得,提高了算法效率;并且该算法实现了在重建超分辨SAR图像的同时获取高精度的目标散射中心属性级特征。仿真合成数据和电磁计算数据验证了算法的超分辨能力,并利用ASC属性的克拉美罗界对算法属性估计性能进行了评估。

The applications in both the military and civil field have pressing needs and great expectations of achieving the target higher resolution and more detailed description.This paper firstly modeled the object-level SAR observations based on attributed scattering center(ASC)model in sparse representation framework.Secondly,it proposed a classifying strategy of the target attributes space for the object-level reconstruction in signal domain.Combined with data extrapolating,then it proposed a stochastic gradient minimum variance pursuit(SGMVP)based object-level super-resolution reconstruction algorithm.It finally achieved super-resolution image by FFT to effectively promotethe efficiency of the proposed algorithm.The proposed algorithm not only can achieve improved super-resolution image,but also provide accurate physically-relevant attributed features of the scatterers simultaneously.Experimental results confirm the effectiveness of the proposed algorithm.