An Electromagnetic Scattering Mechanism Recognition Method Based on Deep Learning
DOI:
https://doi.org/10.13052/2025.ACES.J.400102Keywords:
Convolutional neural network, deep learning, electromagnetic scattering mechanisms, recognitionAbstract
In this paper, we proposed a data-driven deep learning (DL) method to recognize various electromagnetic (EM) scattering mechanisms. With appropriate training data containing different EM scattering mechanisms, the proposed network can accurately recognize the EM scattering mechanisms of complex models. Numerical experiments show that the DL network architecture is effective for both vertical polarization and horizontal polarization scattered field, and the average relative recognition error of the proposed method is less than 5%. This paper shows that deep neural networks have a good learning capacity for EM scattering mechanism recognition. This provides a research strategy for solving EM scattering mechanism identification in more complex EM environments.
Downloads
References
J. Li and X. Liu, “A method of decomposition and extraction of scattering mechanisms based on time slot difference,” IEEE Transactions on Antennas and Propagation, vol. 69, no. 3, pp. 1560-1568, Mar. 2021.
X. Liu, J. Li, Y. Zhu, and S. Zhang, “Scattering characteristic extraction and recovery for multiple targets based on time frequency analysis,” Applied Computational Electromagnetics Society (ACES) Journal, vol. 35, no. 8, pp. 962-970, Aug. 2020.
W. Gordon, “Far-field approximations to the Kirchoff-Helmholtz representations of scattered fields,” IEEE Transactions on Antennas and Propagation, vol. 23, no. 4, pp. 590-592, July1975.
M. Kara and M. Mutlu, “Scattering and diffraction evaluated by physical optics surface current on a truncated cylindrical conductive cap,” Applied Computational Electromagnetics Society (ACES) Journal, vol. 38, no. 05, pp. 304-308, May2023.
H. Liu, B. Jiu, F. Li, and Y. Wang, “Attributed scattering center extraction algorithm based on sparse representation with dictionary refinement,” IEEE Transactions on Antennas and Propagation, vol. 65, no. 5, pp. 2604-2614, May2017.
X.-Y. He, G.-D. Tong, W. Gao, X.-L. Mi, P.-C. Gao, and Y. Zhang, “The method of adaptive Gaussian decomposition-based recognition and extraction of scattering mechanisms,” in 2018 12th International Symposium on Antennas, Propagation and EM Theory (ISAPE), Hangzhou, China, pp. 1-4, 2018.
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, Apr.2019.
D. He, W. Guo, T. Zhang, Z. Zhang, and W. Yu, “Occluded target recognition in SAR imagery with scattering excitation learning and channel dropout,” IEEE Geoscience and Remote Sensing Articles, vol. 20, pp. 1-5, 2023.
N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov, “Deep learning classification of land cover and crop types using remote sensing data,” IEEE Geoscience and Remote Sensing Articles, vol. 14, no. 5, pp. 778-782, May 2017.
F. A. Molinet, “Modern high frequency techniques for RCS computation: A comparative analysis,” Applied Computational Electromagnetics Society (ACES) Journal, vol. 6, no. 1, pp. 31-58, July2022.
J. Perez and M. F. Catedra, “Application of physical optics to the RCS computation of bodies modeled with NURBS surfaces,” IEEE Transactions on Antennas and Propagation, vol. 42, no. 10, pp. 1404-1411, Oct. 1994.
H. Ling, R.-C. Chou, and S.-W. Lee, “Shooting and bouncing rays: Calculating the RCS of an arbitrarily shaped cavity,” IEEE Transactions on Antennas and Propagation, vol. 37, no. 2, pp. 194-205, Feb. 1989.
M. D. Desai and W. K. Jenkins, “Convolution back projection image reconstruction for spotlight mode synthetic aperture radar,” IEEE Transactions on Image Processing, vol. 1, no. 4, pp. 505-517, Oct. 1992.
D. P. Kingma and J. Ba, ADAM: A Method for Stochastic Optimization [Online]. Available: https://arxiv.org/abs/1412.6980
M. Martorella, N. Acito, and F. Berizzi, “Statistical CLEAN technique for ISAR imaging,” IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 11, pp. 3552-3560, Nov.2007.
