An Electromagnetic Imaging Algorithm Based on Generative Adversarial Network for Limited Observation Angle

Authors

  • Chun Xia Yang Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University, Shanghai 200234, China
  • Xirui Yang Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University, Shanghai 200234, China, School of Electronic and Information Engineering Guangzhou City University of Technology, Guangzhou 510800, China
  • Jian Zhang Xpeedic Technology, Inc. Shanghai 201210, China
  • Chi Zhou Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University, Shanghai 200234, China
  • Mei Song Tong Department of Electronic Science and Technology Tongji University, Shanghai 201804, China

DOI:

https://doi.org/10.13052/2024.ACES.J.400803

Keywords:

diffraction tomography (DT), generative adversarial network (GAN), inverse scattering, limited angle

Abstract

In the context of long-distance detection and obstacle occlusion, the limited observation angle of electromagnetic imaging poses significant challenges for accurate reconstruction. To address this issue, we propose a hybrid electromagnetic reconstruction algorithm based on a generative adversarial network (GAN). This algorithm utilizes the diffraction tomography (DT) method to generate an initial image, which serves as input for the GAN. Through adversarial training between the generator and the discriminator, the algorithm produces a reconstructed image with enhanced accuracy. Firstly, unlike complete learning-based reconstruction methods that rely solely on scattering field data, our approach effectively integrates both scattering characteristics and a priori information from the DT image model, thus improving the accuracy and generalizability of the neural network. Secondly, compared to other linear approximation algorithms, the DT algorithm incorporates fast Fourier transform (FFT) to enhance computational efficiency. Thirdly, this study employs a Fourier spatial data extrapolation technique to mitigate the limitations of insufficient data and improve imaging fidelity. Numerical simulations demonstrate that even at a narrow observation angle of 900, the proposed algorithm exhibits excellent reconstruction performance and notable generalization ability.

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Author Biographies

Chun Xia Yang, Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University, Shanghai 200234, China

Chun Xia Yang received the Ph.D. degree in electronic science and technology from Tongji University, Shanghai, China, in 2017. During her doctoral studies, she also conducted research at the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA as a visiting student between 2014 and 2016. She is currently an associate professor at the Department of Communication Engineering, Shanghai Normal University, Shanghai, China. Her ongoing research interests primarily revolve around electromagnetic inverse scattering for imaging and computational electromagnetics.

Xirui Yang, Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University, Shanghai 200234, China, School of Electronic and Information Engineering Guangzhou City University of Technology, Guangzhou 510800, China

Xirui Yang received her B.S. degree in Communication Engineering and M.S. degree in Electronic Information from Shanghai Normal University, Shanghai, China, in 2021 and 2024, respectively. She is currently affiliated with the School of Electronic and Information Engineering, Guangzhou City University of Technology, Guangzhou, China. Her research focuses primarily on electromagnetic inverse scattering.

Jian Zhang, Xpeedic Technology, Inc. Shanghai 201210, China

Jian Zhang received his B.S. degree in Electronics Science and Technology and his Ph.D. degree in Control Science and Engineering from Tongji University, Shanghai, China, in 2014 and 2020, respectively. He is currently working as an Engineer at Xpeedic Technology, Inc., Shanghai, China. His research mainly focuses on multi-physics numerical simulation techniques.

Chi Zhou, Shanghai Engineering Research Center of Intelligent Education and Bigdata Shanghai Normal University, Shanghai 200234, China

Chi Zhou received the M.S. degree in Electronic Information from Shanghai Normal University, Shanghai, China, in 2025. He is currently affiliated with Wenyin Cloud Computing Co., Ltd, Shanghai, China. His research mainly focuses on electromagnetic inverse scattering.

Mei Song Tong, Department of Electronic Science and Technology Tongji University, Shanghai 201804, China

Mei Song Tong received the B.S. and M.S. Degrees from Huazhong University of Science and Technology, Wuhan, China, respectively, and Ph.D. degree from Arizona State University, Tempe, Arizona, USA, all in electrical engineering. He is currently the Distinguished Professor and Head of Department of Electronic Science and Technology, and Vice Dean of College of Microelectronics, Tongji University, Shanghai, China. He has also held an adjunct professorship at the University of Illinois at Urbana-Champaign, Urbana, Illinois, USA, and an honorary professorship at the University of Hong Kong, China. He has published more than 700 papers in refereed journals and conference proceedings and co-authored six books or book chapters. His research interests include electromagnetic field theory, antenna theory and design, simulation and design of RF/microwave circuits and devices, interconnect and packaging analysis, inverse electromagnetic scattering for imaging, and computational electromagnetics.

Prof. Tong is a Fellow of the Electromagnetics Academy, Fellow of the Japan Society for the Promotion of Science (JSPS), and Senior Member (Commission B) of the USNC/URSI. He has been the chair of Shanghai Chapter since 2014 and the chair of SIGHT committee in 2018, respectively, in IEEE Antennas and Propagation Society. He has served as an associate editor or guest editor for several well-known international journals, including IEEE Antennas and Propagation Magazine, IEEE Transactions on Antennas and Propagation, IEEE Transactions on Components, Packaging and Manufacturing Technology, International Journal of Numerical Modeling: Electronic Networks, Devices and Fields, Progress in Electromagnetics Research, and Journal of Electromagnetic Waves and Applications, etc. He also frequently served as a session organizer/chair, technical program committee member/chair, and general chair for some prestigious international conferences. He was the recipient of a Visiting Professorship Award from Kyoto University, Japan, in 2012, and from University of Hong Kong, China, 2013. He advised and coauthored 15 papers that received the Best Student Paper Award from different international conferences. He was the recipient of the Travel Fellowship Award of USNC/URSI for the 31th General Assembly and Scientific Symposium (GASS) in 2014, Advance Award of Science and Technology of Shanghai Municipal Government in 2015, Fellowship Award of JSPS in 2016, Innovation Award of Universities’ Achievements of Ministry of Education of China in 2017, Innovation Achievement Award of Industry-Academia-Research Collaboration of China in 2019, “Jinqiao” Award of Technology Market Association of China in 2020, Baosteel Education Award of China in 2021, Carl Friedrich von Siemens Research Award of the Alexander von Humboldt Foundation of Germany in 2023, and Technical Achievement Award of Applied Computational Electromagnetic Society (ACES) of USA in 2024. In 2018, he was selected as the Distinguished Lecturer (DL) of IEEE Antennas and Propagation Society for 2019-2022.

References

Z. Wei and X. Chen, “Solving Full-Wave Nonlinear Inverse Scattering Problems by Deep Learning Schemes,” in 2019 IEEE International Conference on Computational Electromagnetics (ICCEM), pp. 1-2, IEEE, 2019.

L. Li, L. G. Wang, F. L. Teixeira, C. Liu, A. Nehorai, and T. J. Cui, “DeepNIS: Deep neural network for nonlinear electromagnetic inverse scattering,” IEEE Transactions on Antennas and Propagation, vol. 67, no. 3, pp. 1819-1825, 2018.

X. Chen, Z. Wei, L. Maokun, P. Rocca, et al., “A review of deep learning approaches for inverse scattering problems (invited review),” Electromagnetic Waves, vol. 167, pp. 67-81, 2020.

R. Guo, X. Song, M. Li, F. Yang, S. Xu, and A. Abubakar, “Supervised Descent Learning Technique for 2-D Microwave Imaging,” IEEE Transactions on Antennas and Propagation, vol. 67, no. 5, pp. 3550-3554, 2019.

G. Chen, P. Shah, J. Stang, and M. Moghaddam, “Learning-Assisted Multimodality Dielectric Imaging,” IEEE Transactions on Antennas and Propagation, vol. 68, no. 3, pp. 2356-2369,2020.

Y. Sanghvi, Y. Kalepu, and U. K. Khankhoje, “Embedding Deep Learning in Inverse Scattering Problems,” IEEE Transactions on Computational Imaging, vol. 6, pp. 46-56, 2020.

R. Guo, T. Huang, M. Li, H. Zhang, and Y. C. Eldar, “Physics-Embedded Machine Learning for Electromagnetic Data Imaging: Examining three types of data-driven imaging methods,” IEEE Signal Processing Magazine, vol. 40, no. 2, pp. 18-31, 2023.

K. Xu, Z. Qian, Y. Zhong, J. Su, H. Gao, and W. Li, “Learning-Assisted Inversion for Solving Nonlinear Inverse Scattering Problem,” IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 6, pp. 2384-2395, 2023.

C. X. Yang, J. J. Meng, S. Wei, and M. S. Tong, “A Dual-input Electromagnetic Inverse Scattering Algorithm Based on Improved U-net,” Applied Computational Electromagnetics Society Journal (ACES), pp. 961-969, 2024.

X. Ye, N. Du, D. Yang, X. Yuan, R. Song, S. Sun, and D. Fang, “Application of Generative Adversarial Network-Based Inversion Algorithm in Imaging 2-D Lossy Biaxial Anisotropic Scatterer,” IEEE Transactions on Antennas and Propagation, vol. 70, no. 9, pp. 8262-8275, 2022.

R. Song, Y. Huang, K. Xu, X. Ye, C. Li, and X. Chen, “Electromagnetic Inverse Scattering With Perceptual Generative Adversarial Networks,” IEEE Transactions on Computational Imaging, vol. 7, pp. 689-699, 2021.

M. Li, R. Guo, K. Zhang, Z. Lin, F. Yang, S. Xu, X. Chen, A. Massa, and A. Abubakar, “Machine learning in electromagnetics with applications to biomedical imaging: A review,” IEEE Antennas and Propagation Magazine, vol. 63, no. 3, pp. 39-51, 2021.

Z. Ma, K. Xu, R. Song, C.-F. Wang, and X. Chen, “Learning-Based Fast Electromagnetic Scattering Solver Through Generative Adversarial Network,” IEEE Transactions on Antennas and Propagation, vol. 69, no. 4, pp. 2194-2208, 2021.

T. Negishi, V. Picco, L. L. Monte, and D. Erricolo, “Dyadic contrast function for the forward model of diffraction tomography of thin cylindrical objects,” IEEE Antennas and Wireless Propagation Letters, vol. 16, pp. 991-994, 2016.

C. Yang, J. Zhang, and M. S. Tong, “An FFT-accelerated particle swarm optimization method for solving far-field inverse scattering problems,” IEEE Transactions on Antennas and Propagation, vol. 69, no. 2, pp. 1078-1093, 2020.

A. Aggarwal, M. Mittal, and G. Battineni, “Generative adversarial network: An overview of theory and applications,” International Journal of Information Management Data Insights, vol. 1, no. 1, p. 100004, 2021.

L. Guo, G. Song, and H. Wu, “Complex-valued Pix2pix—Deep neural network for nonlinear electromagnetic inverse scattering,” Electronics, vol. 10, no. 6, p. 752, 2021.

C. Yang, J. Zhang, and M. Tong, “A novel diffraction tomographic algorithm with narrow observation angle,” International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, vol. 32, no. 5, p. e2583, 2019.

W. C. Chew, Waves and Fields in Inhomogenous Media, vol. 16, John Wiley & Sons, 1999.

K. Belkebir, P. C. Chaumet, and A. Sentenac, “Superresolution in total internal reflection tomography,” J. Opt. Soc. Am. A, vol. 22, no. 9, pp. 1889-1897, Sep. 2005.

Z. Wei and X. Chen, “Deep-learning schemes for full-wave nonlinear inverse scattering problems,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 4, pp. 1849-1860,2018.

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM, vol. 63, no. 11, pp. 139-144, 2020.

S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, “CBAM: Convolutional Block Attention Module,” in Proceedings of the European Conference on Computer Vision (ECCV), Sep. 2018.

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, editors, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pp. 234-241, Springer International Publishing, Cham, 2015.

C. X. Yang, J. Zhang, and M. S. Tong, “A hybrid inversion method based on the bat algorithm for microwave imaging of two-dimensional dielectric scatterers,” Progress in Electromagnetics Research M, vol. 102, pp. 91-104, 2021.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.

K. Belkebir and M. Saillard, “Testing inversion algorithms against experimental data,” Inverse problems, vol. 17, no. 6, p. 1565, 2001.

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Published

2025-08-30

How to Cite

[1]
C. X. . Yang, X. . Yang, J. . Zhang, C. . Zhou, and M. S. . Tong, “An Electromagnetic Imaging Algorithm Based on Generative Adversarial Network for Limited Observation Angle”, ACES Journal, vol. 40, no. 08, pp. 702–713, Aug. 2025.

Issue

2025: Vol 40 Iss 8

Section

General Submission

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