论文标题
线性模型启发的电磁反射神经网络
Linear-Model-inspired Neural Network for Electromagnetic Inverse Scattering
论文作者
论文摘要
电磁逆散射问题（ISP）旨在从散射测量中检索介电散射器的拼写。这通常是高度非线性的，因此很难解决问题。为了减轻问题，这封信利用了基于线性模型的网络（LMN）学习策略，该策略从模型的复杂性和数据学习中受益。通过引入ISP的线性模型，提出了具有网络驱动常规式启动的新模型。为了获得有效的端到端学习，提出了网络体系结构和超参数估计。实验结果证明了其优势与某些最先进的事物。
Electromagnetic inverse scattering problems (ISPs) aim to retrieve permittivities of dielectric scatterers from the scattering measurement. It is often highly nonlinear, caus-ing the problem to be very difficult to solve. To alleviate the issue, this letter exploits a linear model-based network (LMN) learning strategy, which benefits from both model complexity and data learning. By introducing a linear model for ISPs, a new model with network-driven regular-izer is proposed. For attaining efficient end-to-end learning, the network architecture and hyper-parameter estimation are presented. Experimental results validate its superiority to some state-of-the-arts.
