On an artificial neural network for inverse scattering problems
On an artificial neural network for inverse scattering problems
复制标题
求解逆散射问题的人工神经网络
DOI：
10.1016/j.jcp.2021.110771
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发表时间：
2021-10-22
影响因子：
4.1
通讯作者：
Zhang, Kai
中科院分区：
文献类型：
--
作者：
Gao, Yu;Liu, Hongyu;Zhang, Kai
本文考虑用人工神经网络求解逆散射问题。作为一个工作模型，我们考虑从（可能的）有限孔径雷达截面（RCS）数据中恢复单个入射场对应的散射目标的逆问题。由于信息的严重缺乏，这种非线性不适定逆问题具有重要的实际意义和挑战性。从几何和物理的角度来看，低频数据应该能够解决唯一可识别性问题，但同时也会失去分辨率。另一方面，机器学习可以用来突破分辨率限制。结合这两种观点，我们开发了一种全连接神经网络（FCNN）来解决反问题。大量的数值结果表明，该方法能产生令人惊叹的重建效果。该方法可以推广到其他测量信息有限的逆散射问题。(c) 2021爱思唯尔公司版权所有。上标或下标可用
In this paper, we consider artificial neural networks for inverse scattering problems. As a working model, we consider the inverse problem of recovering a scattering object from the (possibly) limited-aperture radar cross section (RCS) data collected corresponding to a single incident field. This nonlinear and ill-posed inverse problem is practically important and highly challenging due to the severe lack of information. From a geometrical and physical point of view, the low-frequency data should be able to resolve the unique identifiability issue, but meanwhile lose the resolution. On the other hand, the machine learning can be used to break through the resolution limit. By combining the two perspectives, we develop a fully connected neural network (FCNN) for the inverse problem. Extensive numerical results show that the proposed method can produce stunning reconstructions. The proposed strategy can be extended to tackling other inverse scattering problems with limited measurement information. (c) 2021 Elsevier Inc. All rights reserved.Superscript/Subscript Available