Journal
IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS
Volume 18, Issue 11, Pages 2254-2258Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LAWP.2019.2925578
Keywords
Convolutional neural network; electromagnetic inverse scattering (EMIS); high-contrast object; residual learning; two-step process
Funding
- Research Grants Council of Hong Kong [GRF17207114, GRF17210815]
- Aerospace Research andDevelopment (AOARD) [FA2386-17-1-0010]
- National Natural Science Foundation (NSFC) [61271158]
- Hong Kong University Grants Committee (UGC) [AoE/P-04/08]
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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 (permittivities) 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.
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