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Linear electromagnetic inverse scattering via generative adversarial networks

Published online by Cambridge University Press:  01 October 2021

Huilin Zhou ,
Huimin Zheng Open the ORCID record for Huimin Zheng [Opens in a new window] ,
Qiegen Liu ,
Jian Liu Open the ORCID record for Jian Liu [Opens in a new window]  and
Yuhao Wang
Huilin Zhou
Affiliation:
School of Information Engineering, Nanchang University, 999 Xuefu Avenue, Honggutan New District, Nanchang, Jiangxi, China
Huimin Zheng
Affiliation:
School of Information Engineering, Nanchang University, 999 Xuefu Avenue, Honggutan New District, Nanchang, Jiangxi, China
Qiegen Liu
Affiliation:
School of Information Engineering, Nanchang University, 999 Xuefu Avenue, Honggutan New District, Nanchang, Jiangxi, China
Jian Liu
Affiliation:
School of Information Engineering, Nanchang University, 999 Xuefu Avenue, Honggutan New District, Nanchang, Jiangxi, China
Yuhao Wang*
Affiliation:
School of Information Engineering, Nanchang University, 999 Xuefu Avenue, Honggutan New District, Nanchang, Jiangxi, China
*
Author for correspondence: Yuhao Wang, E-mail: wangyuhao@ncu.edu.cn
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Abstract

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.

Keywords

Deep iterationelectromagnetic inverse scatteringgenerative adversarial networklinear model
Type
Radar
Information
International Journal of Microwave and Wireless Technologies , Volume 14 , Issue 9 , November 2022 , pp. 1168 - 1176
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press in association with the European Microwave Association

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