Linear-Model-Inspired Neural Network for Electromagnetic Inverse Scattering

Abstract

Electromagnetic inverse scattering problems (ISPs) aim to retrieve permittivities of dielectric scatterers from the scattering measurement. It is often highly nonlinear, causing 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 regularizer 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 art.