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.
@article{arxiv.2003.01465,
title = {Linear-Model-inspired Neural Network for Electromagnetic Inverse Scattering},
author = {Huilin Zhou and Tao Ouyang and Yadan Li and Jian Liu and Qiegen Liu},
journal= {arXiv preprint arXiv:2003.01465},
year = {2023}
}