Electromagnetic inverse-scattering problems (ISPs) are concerned with determining the properties of an unknown object using measured scattered fields. ISPs are often highly nonlinear, causing the problem to be very difficult to address. In addition, the reconstruction images of different optimization methods are distorted which leads to inaccurate reconstruction results. To alleviate these issues, we propose a new linear model solution of generative adversarial network-based (LM-GAN) inspired by generative adversarial networks (GAN). Two sub-networks are trained alternately in the adversarial framework. A linear deep iterative network as a generative network captures the spatial distribution of the data, and a discriminative network estimates the probability of a sample from the training data. Numerical results validate that LM-GAN has admirable fidelity and accuracy when reconstructing complex scatterers.

电磁逆散射问题 (ISP) 涉及使用测量的散射场确定未知物体的属性。ISP 通常是高度非线性的，导致问题很难解决。此外，不同优化方法的重建图像失真，导致重建结果不准确。为了缓解这些问题，我们提出了一种新的基于生成对抗网络（LM-GAN）的线性模型解决方案，其灵感来自生成对抗网络（GAN）。在对抗框架中交替训练两个子网络。线性深度迭代网络作为生成网络捕获数据的空间分布，判别网络从训练数据中估计样本的概率。