基于迭代多尺度深度网络的非线性逆散射成像方法  

Nonlinear inverse scattering imaging method based on iterative multi-scale network

作　　者：周辉林[1] 黄鑫[1] 王玉皞[1] ZHOU Huilin;HUANG Xin;WANG Yuhao(Institute of Artificial Intelligence Industry Research,Nanchang University,Nanchang 330031,China)

机构地区：[1]南昌大学人工智能工业研究院,南昌330031

出　　处：《电波科学学报》2022年第6期1019-1024,1094,共7页Chinese Journal of Radio Science

基　　金：国家自然科学基金(62161027,62061030)。

摘　　要：传统的迭代多尺度方法(iterative multiscaling approach,IMA)在求解非线性电磁场逆散射问题时,可以自适应提高成像空间的分辨率,缓解逆问题的病态性,但容易陷入局部极小值且无法做到实时重构.文中提出了一种迭代多尺度深度网络,该网络结合传统IMA和深度网络的优势,将IMA展开成深度网络模型(命名为IMA-Net).该方法迭代地执行一种感兴趣区域(regions of interest,ROI)提取算法,在不同尺度的ROI内构建目标函数,并将目标函数分解成若干个优化子问题,子问题的迭代更新过程映射到深度网络结构中,交替更新相关分量,求解出目标函数的最优值.实验结果验证了该方法的有效性和优越性,为目标实时重构提供了一个有效方案.The combination of iterative multiscaling approach(IMA)and deep learning to solve the problem of electromagnetic inverse scattering is of great significance.Based on the IMA framework,this paper proposes a multi-scale iterative unrolling network to solve the nonlinear inverse scattering problem,termed IMA-Net.The network iteratively executes a region of interest(ROI)extraction algorithm,defines an objective function within RoI of different scales,and decomposes the objective function into several optimization sub-problems.The iterative update process of the sub-problems is mapped into the deep network structure,and the relevant components are alternately updated to find the optimal value of the objective function.The results show the accuracy and superiority of proposed method.The studies provide an effective scheme for real-time reconstruction of unknown scatterers.

关 键 词：非线性逆散射 迭代多尺度方法(IMA) 深度网络 

分 类 号：O451[理学—无线电物理]