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2022-11-10
Network Optimization Algorithm for Radar Active Jamming Identification Based on Neural Architecture Search
By
Progress In Electromagnetics Research C, Vol. 126, 183-196, 2022
Abstract
A network optimization approach based on Neural Architecture Search (NAS) and network pruning is suggested to solve the issue of poor recognition performance of missile-borne radar active jamming under the condition of short sample sizes. The approach realized the ideal network design under severely constrained technical indications by combining the benefits of several methods, including NAS, convolutional light-weighting, and network pruning. The recognition network's convolution kernel size parameters were first optimized using NAS. The number of model parameters were then decreased via convolutional substitution. Finally, the structured pruning algorithm further screened the redundant network based on the technical indicators. The WideResNet28_2 wide residual network's recognition accuracy is only 84.38% when there are only 1000 training samples for each type of signal, according to the simulation results. After optimization, the number of new model parameters was increased 2.55M, 2.26M, and 1.78M, respectively, and their respective recognition accuracy was increased to 85.7%, 85.61%, and 85.37%. According to the simulation results, the technique offers a wide range of possible applications in the optimized design of radar active jamming identification networks for small sample sizes.
Citation
Zejun Gao, Fei Cao, Chuan He, Xiaowei Feng, Jianfeng Xu, and Jianqiang Qin, "Network Optimization Algorithm for Radar Active Jamming Identification Based on Neural Architecture Search," Progress In Electromagnetics Research C, Vol. 126, 183-196, 2022.
doi:10.2528/PIERC22081806
References

1. Li, N. J. and Y. T. Zhang, "A survey of radar ECM and ECCM," IEEE Transactions on Aerospace and Electronic Systems, Vol. 31, No. 3, 1110-1120, 1995.
doi:10.1109/7.395232

2. Krizhevsky, A., I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in Neural Information Processing Systems, Vol. 25, 2012.

3. Sun, J., G. Xu, W. Ren, et al. "Radar emitter classification based on unidimensional convolutional neural network," Radar, Sonar & Navigation, Vol. 12, No. 8, 862-867, IET, 2018.
doi:10.1049/iet-rsn.2017.0547

4. Liu, W., Z. Wang, X. Liu, et al. "A survey of deep neural network architectures and their applications," Neurocomputing, Vol. 234, 11-26, Apr. 19, 2017.

5. Yu, J., J. Li, B. Sun, et al. "Barrage jamming detection and classification based on convolutional neural network for synthetic aperture radar," IGARSS 2018 --- 2018 IEEE International Geoscience and Remote Sensing Symposium, 4583-4586, IEEE, 2018.

6. Liu, S. and C. Zhu, "Jamming recognition based on feature fusion and convolutional neural network," Journal of Beijing Institute of Technology, Vol. 31, No. 2, 9, 2022.

7. He, K., X. Zhang, S. Ren, et al. "Deep residual learning for image recognition," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770-778, 2016.

8. Sun, C., A. Shrivastava, S. Singh, et al. "Revisiting unreasonable effectiveness of data in deep learning era," Proceedings of the IEEE International Conference on Computer Vision, 843-852, 2017.

9. Saptarshi, M. and J. Maiti, "Risk analysis using FMEA: Fuzzy similarity value and possibility theory based approach," Expert Systems with Applications, Vol. 41, No. 7, 3527-3537, 2014.
doi:10.1016/j.eswa.2013.10.058

10. Versaci, M., G. Angiulli, P. Crucitti, et al. "A Fuzzy similarity-based approach to classify numerically simulated and experimentally detected carbon fiber-reinforced polymer plate defects," Sensors, Vol. 22, 4232, 2022.
doi:10.3390/s22114232

11. Zadeh, L. A., "A note on similarity-based definitions of possibility and probability," Information Sciences, Vol. 267, 334-336, 2014.
doi:10.1016/j.ins.2014.01.046

12. Termritthikun, C., Y. Jamtsho, J. Ieamsaard, et al. "EEEA-Net: An early exit evolutionary neural architecture search," Engineering Applications of Artificial Intelligence, Vol. 104, No. 2, 104397, 2021.
doi:10.1016/j.engappai.2021.104397

13. Yao, X., Y. Shi, G. Huo, et al. "Lightweight model construction based on neural architecture search," Pattern Recognition and Artificial Intelligence, Vol. 34, No. 11, 1038-1048, 2021.

14. Liu, H., K. Simonyan, and Y. Yang, "DARTS: Differentiable architecture search,", arXiv preprint arXiv:1806.09055, 2018.

15. Reed, R. D., "Pruning algorithms --- A survey," IEEE Transactions on Neural Networks, 1993.

16. Li, H., A. Kadav, I. Durdanovic, et al. "Pruning filters for efficient convnets,", arXiv preprint arXiv:1608.08710, 2016.

17. He, Y., X. Zhang, and J. Sun, "Channel pruning for accelerating very deep neural networks," Proceedings of the IEEE International Conference on Computer Vision, 1389-1397, 2017.

18. He, Y., G. Kang, X. Dong, et al. "Soft filter pruning for accelerating deep convolutional neural networks,", arXiv preprint arXiv:1808.06866, 2018.

19. Ma, J., Y. Zhang, Z. Ma, et al. "Research progress of lightweight neural network convolution design," Journal of Frontiers of Computer Science and Technology, Vol. 16, No. 3, 17, 2022.

20. Dumoulin, V. and F. Visin, "A guide to convolution arithmetic for deep learning,", arXiv preprint arXiv:1603.07285, 2016.

21. Szegedy, C., V. Vanhoucke, S. Ioffe, et al. "Rethinking the inception architecture for computer vision," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2818-2826, 2016.

22. Wang, X., J.-C. Liu, W. Zhang, et al. "The mathematical principle of intermittent sampling and forwarding interference," Science in China (Series E), Vol. 36, 891-901, 2006.

23. Suo, W., "Research on jamming against pseudo-random code pulse-doppler fuse,", Xidian University, Shaanxi, 2014.

24. Zhou, C., Q. Liu, and C. Hu, "Time-frequency analysis techniques for recognition and suppression of interrupted sampling repeater jamming," Journal of Radars, Vol. 8, 100-106, 2019.

25. Yu, H., X. Yan, R. Jia, et al. "Research on anti-jamming performance of M-sequence pseudo-random code phase modulation pulse doppler fuze," ACTA Armamentarii, Vol. 41, No. 03, 417-425, 2020.

26. Toole, J. M. O. and B. Boashash, "Fast and memory-efficient algorithms for computing quadratic time-frequency distributions," Applied & Computational Harmonic Analysis, Vol. 35, No. 2, 350-358, 2013.
doi:10.1016/j.acha.2013.01.003

27. Gao, Z., F. Cao, C. He, et al. "MATLAB simulation of performance evaluation model of time-frequency analysis method based on SVM," 2022 8th International Symposium on Sensors, Mechatronics and Automation System, Suzhou, China, 2022.

28. Zhang, X., Modern Signal Processing (Third Edition), Tsinghua University Press, Beijing, 2015.

29. Recht, B., R. Roelofs, L. Schmidt, et al. "Do cifar-10 classifiers generalize to cifar-10?,", arXiv preprint arXiv:1806.00451, 2018.

30. Zagoruyko, S. and N. Komodakis, "Wide residual networks,", arXiv preprint arXiv:1605.07146, 2016.

31. Sohn, K., D. Berthelot, N. Carlini, et al. "Fixmatch: Simplifying semi-supervised learning with consistency and confidence," Advances in Neural Information Processing Systems, Vol. 33, 596-608, 2020.