1. WG Carrara, RS Goodman and RM Majewski, Spotlight Synthetic Aperture Radar: Signal Processing Algorithms. Norwood, MA: Artech House, 1995.
2. P Tait, Introduction to Radar Target Recognition. Stevenage, UK: Institution of Engineering and Technology, 2009.
3. D Blacknell and H Griffiths, Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR). Stevenage, UK: Institution of Engineering and Technology, 2013.
4. CC Chen and HC Andrews, "Target-motion-induced radar imaging,"
IEEE Transactions on Aerospace and Electronic Systems, vol. AES-16, no. 1, pp. 2–14, 1980.
5. RF Harrington, Field Computation by Moment Methods. Piscataway, NJ: Wiley-IEEE Press, 1993.
6. PP Silvester and RL Ferrari, Finite Elements for Electrical Engineers. 3rd ed. New York, NY: Cambridge University Press, 1996.
7. PP Silvester, "Universal finite element matrices for tetrahedra,"
International Journal for Numerical Methods in Engineering, vol. 18, no. 7, pp. 1055–1061, 1982.
8. ZJ Cendes, "Vector finite elements for electromagnetic field computation,"
IEEE Transactions on Magnetics, vol. 27, no. 5, pp. 3958–3966, 1991.
9. K Yee, "Numerical solution of initial boundary value problems involving maxwell’s equations in isotropic media,"
IEEE Transactions on Antennas and Propagation, vol. 14, no. 3, pp. 302–307, 1966.
10. A Taflove, "Application of the finite-difference time-domain method to sinusoidal steady-state electromagnetic-penetration problems,"
IEEE Transactions on Electromagnetic Compatibility, vol. EMC-22, no. 3, pp. 191–202, 1980.
11. G Mur, "Absorbing boundary conditions for the finite-difference approximation of the time-domain electromagnetic-field equations,"
IEEE Transactions on Electromagnetic Compatibility, vol. 23, no. 4, pp. 377–382, 1981.
12. JP Berenger, "A perfectly matched layer for the absorption of electromagnetic waves,"
Journal of Computational Physics, vol. 114, no. 2, pp. 185–200, 1994.
13. JM Song and WC Chew, "Multilevel fast-multipole algorithm for solving combined field integral equations of electromagnetic scattering,"
Microwave and Optical Technology Letters, vol. 10, no. 1, pp. 14–19, 1995.
14. SM Seo and JF Lee, "A fast IE-FFT algorithm for solving PEC scattering problems,"
IEEE Transactions on Magnetics, vol. 41, no. 5, pp. 1476–1479, 2005.
15. SM Seo, CF Wang and JF Lee, "Analyzing PEC scattering structure using an IE-FFT algorithm," Applied Computational Electromagnetics Society Journal, vol. 24, no. 2, pp. 116–128, 2009.
16. C Farhat, J Mandel and FX Roux, "Optimal convergence properties of the FETI domain decomposition method,"
Computer Methods in Applied Mechanics and Engineering, vol. 115, no. 3–4, pp. 365–385, 1994.
17. C Guiffaut and K Mahdjoubi, "A parallel FDTD algorithm using the MPI library,"
IEEE Antennas and Propagation Magazine, vol. 43, no. 2, pp. 94–103, 2001.
18. H Ling, RC Chou and SW 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, 1989.
19. PY Ufimtsev, "Improved physical theory of diffraction: removal of the grazing singularity,"
IEEE Transactions on Antennas and Propagation, vol. 54, no. 10, pp. 2698–2702, 2006.
20. RG Kouyoumjian and PH Pathak, "A uniform geometrical theory of diffraction for an edge in a perfectly conducting surface,"
Proceedings of the IEEE, vol. 62, no. 11, pp. 1448–1461, 1974.
21. M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro et al.,
TensorFlow: large-scale machine learning on heterogeneous distributed systems, 2016;[Online]. Available:
https://arxiv.org/abs/1603.04467
.
22. A Paszke, S Gross, F Massa, A Lerer, J Bradbury, G Chanan et al., "PyTorch: an imperative style, high-performance deep learning library," In: Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS); Vancouver, Canada. 2019; pp 8024–8035.
23. R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas et al.,
Theano: a python framework for fast computation of mathematical expressions, 2016;[Online]. Available:
https://arxiv.org/abs/1605.02688
.
24. F Chollet,
Keras: the python deep learning library, 2018;[Online]. Available:
https://keras.io
.
25. Y Jia, E Shelhamer, J Donahue, S Karayev, J Long, RB Girshick, S Guadarrama and TJ Darrell, "Caffe: convolutional architecture for fast feature embedding," In: Proceedings of the 22nd ACM International Conference on Multimedia; Orlando, FL. 2014; pp 675–678.
26. 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.
27. K Simonyan and A Zisserman, "Very deep convolutional networks for large-scale image recognition," In: Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015); San Diego, CA. 2015.
28. P Isola, JY Zhu, T Zhou and AA Efros, "Image-to-image translation with conditional adversarial networks," In:
Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Honolulu, HI. 2017; pp 5967–5976.
29. IJ Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, S Ozair, A Courville and Y Bengio, "Generative adversarial nets," In: Proceedings of the 27th International Conference on Neural Information Processing Systems; Montreal, Canada. 2014; pp 2672–2680.
30. JY Zhu, T Park, P Isola and AA Efros, "Unpaired image-to-image translation using cycle-consistent adversarial networks," In:
Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV); Venice, Italy. 2017; pp 2242–2251.
31. M Ma, J Chen, W Liu and W Yang, "Ship classification and detection based on CNN using GF-3 SAR images,"
Remote Sensing, vol. 10, no. 12, article no. 2043, 2018;
https://doi.org/10.3390/rs10122043
.
32. S Bao, J Meng, L Sun and Y Liu, "Detection of ocean internal waves based on Faster R-CNN in SAR images,"
Journal of Oceanology and Limnology, vol. 38, no. 1, pp. 55–63, 2020.
33. YL Chang, A Anagaw, L Chang, YC Wang, CY Hsiao and WH Lee, "Ship detection based on YOLOv2 for SAR imagery,"
Remote Sensing, vol. 11, no. 7, article no. 786, 2019;
https://doi.org/10.3390/rs11070786
.
34. A Krizhevsky, I Sutskever and GE Hinton, "ImageNet classification with deep convolutional neural networks,"
Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105, 2012.
35. JR Diemunsch and J Wissinger, "Moving and stationary target acquisition and recognition (MSTAR) model-based automatic target recognition: search technology for a robust ATR," In: Proceedings of SPIE vol. 3370: Algorithms for Synthetic Aperture Radar Imagery V; Bellingham, WA, International Society for Optics and Photonics. 1998; pp 481–491.
36. DP Kingma and JL Ba, "Adam: a method for stochastic optimization," In: Proceedings of the 3rd International Conference on Learning Representations (ICLR); San Diego, CA. 2015.