Abstract
In this letter, a new deep learning (DL) approach is proposed to solve the electromagnetic inverse scattering (EMIS) problems. The conventional methods for solving inverse problems face various challenges including strong ill-conditions, high contrast, expensive computation cost, and unavoidable intrinsic nonlinearity. To overcome these issues, we propose a new two-step machine learning based approach. In the first step, a complex-valued deep convolutional neural network is employed to retrieve initial contrasts (permittivity's) of dielectric scatterers from measured scattering data. In the second step, the previously obtained contrasts are input into a complex-valued deep residual convolutional neural network to refine the reconstruction of images. Consequently, the EMIS problem can be solved with much higher accuracy even for high-contrast objects. Numerical examples have demonstrated the capability of the newly proposed method with the improved accuracy. The proposed DL approach for EMIS problem serves a new path for realizing real-time quantitative microwave imaging for high-contrast objects.
Recommended Citation
H. M. Yao et al., "Two-Step Enhanced Deep Learning Approach For Electromagnetic Inverse Scattering Problems," IEEE Antennas and Wireless Propagation Letters, vol. 18, no. 11, pp. 2254 - 2258, article no. 8747485, Institute of Electrical and Electronics Engineers, Nov 2019.
The definitive version is available at https://doi.org/10.1109/LAWP.2019.2925578
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Convolutional neural network; electromagnetic inverse scattering (EMIS); high-contrast object; residual learning; two-step process
International Standard Serial Number (ISSN)
1548-5757; 1536-1225
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Nov 2019
Comments
National Natural Science Foundation of China, Grant FA2386-17-1-0010