volume | PIER Journals

Abstract

Due to the restriction of the low-resolution systems and the interference of background clutter and environmental noise in the exploration process, the traditional classification and recognition algorithms of conventional radar for aircraft targets have low accuracy and poor feature stability. To solve the above problems, this paper proposes to apply high-order cumulant spectrum and deep convolutional neural network (CNN) to feature the extraction and classification of aircraft target radar echoes. Firstly, analyze the high-order statistical characteristics of aircraft echoes, calculate their bispectra, and then enhance the generated bispectrum dataset. Finally, use the augmented dataset to train and test the deep CNN, and obtain the final classification and recognition results. Experimental results show that the proposed method can accurately classify and identify multiple aircraft targets in the dataset, indicating that the bispectral features can better reflect the target characteristics, and the classification method combined with the deep learning model has good classification and identification performance and noise robustness.

1. Zhu, K. F., J. G. Wang, and W. Q. Yie, "Low-resolution radar target recognition algorithm under unbalanced samples," Computer Simulation, Vol. 38, No. 3, 10-14+185, 2021. Google Scholar

2. Li, Q. S., X. C. Xie, H. Zhu, et al. "Analysis of fractal characteristics and target classification of low-resolution radar aircraft echoes in fractional Fourier domain," Application Research of Computers, Vol. 35, No. 9, 2869-2872+2876, 2018. Google Scholar

3. Yang, S. F., H. K.Wu, X.Wang, et al. "Method for modulation feature extraction and classification and recognition of low resolution radar target," Electronic Information Warfare Technology, Vol. 30, No. 4, 15-20, 2015. Google Scholar

4. Zhang, H. and Q. Li, "Target classification with low-resolution radars based on multifractal correlation characteristics in fractional Fourier domain," Progress In Electromagnetics Research C, Vol. 94, 161-176, 2019.
doi:10.2528/PIERC19040702 Google Scholar

5. Hu, J., Q. Li, Q. Zhang, and Y. Zhong, "Aircraft target classification method based on EEMD and multifractal," Progress In Electromagnetics Research M, Vol. 99, 223-231, 2021.
doi:10.2528/PIERM20101802 Google Scholar

6. Xia, S. Q., C. W. Zhang, W. Y. Cai, et al. "Aircraft target classification method for conventional narrowband radar based on micro-doppler effect," Mathematical Problems in Engineering, Vol. 2022, 3154854, 2022. Google Scholar

7. Chen, F., H. W. Liu, L. Du, et al. "Target classification with low-resolution radar based on dispersion situations of eigenvalue spectra," Science China: Information Sciences, Vol. 53, 1446-1460, 2010.
doi:10.1007/s11432-010-3099-5 Google Scholar

8. Li, Q. S. and W. X. Xie, "Target classification with low-resolution surveillance radars based on multifractal features," Progress In Electromagnetics Research B, Vol. 45, 291-308, 2012.
doi:10.2528/PIERB12091509 Google Scholar

9. Li, Q. S. and H. X. Zhang, "Airborne aircraft target classification method based on VFDT feature," Radar Science and Technology, Vol. 18, No. 4, 438-442, 2020. Google Scholar

10. Ding, J. J. and X. D. Zhang, "Research on JEM feature analysis and target classification of conventional radar," Journal of Electronics and Information Technology, Vol. 25, No. 7, 956-962, 2003. Google Scholar

11. Walton, E. K. and I. Jouny, "Bispectrum of radar signatures and application to target classification," Radio Science, Vol. 25, No. 2, 101-113, 1990.
doi:10.1029/RS025i002p00101 Google Scholar

12. Ji, H. B., J. Li, W. X. Xie, et al. "Bispectrum-based radar target classification," Fourth International Conference on Signal Processing, Vol. 1, 419-422, Beijing, China, 1998. Google Scholar

13. Jouny, I., E. D. Garber, and R. L. Moses, "Radar target identification using the bispectrum: A comparative study," IEEE Transactions on Aerospace and Electronic Systems, Vol. 31, No. 1, 69-77, 1995.
doi:10.1109/7.366294 Google Scholar

14. Chen, H. F. and Y. Feng, "Research on CNN-based radar target classification and recognition technology," Modern Radar, Vol. 44, No. 4, 38-43, 2022. Google Scholar

15. Liu, J. E. and F. P. An, "Image classification algorithm based on deep learning-kernel function," Scientific Programming, Vol. 2020, No. 3, 1-14, 2020. Google Scholar

16. He, H. and H. M. Li, "Analysis of high-order spectral characteristics in marine ship multi-noise," Ship Science and Technology, Vol. 38, No. 20, 43-45, 2016. Google Scholar

17. Li, H. and R. J. Hao, "Research on gear fault diagnosis based on correlation entropy and bispectrum analysis," Journal of Vibration Engineering, Vol. 34, No. 5, 1076-1084, 2021. Google Scholar

18. Chen, Z. X., M. X. Chen, M. S. Jiao, et al. "Motor bearing fault diagnosis based on improved EMD and bisspectral analysis," Electric Machines and Control, Vol. 22, No. 5, 78-83, 2018. Google Scholar

19. Mi, X. P., X. H. Chen, Z. Liu, et al. "Dual-spectrum feature identification of radar signals based on entropy evaluation modal decomposition," Systems Engineering and Electronics, Vol. 43, No. 8, 2116-2123, 2021. Google Scholar

20. Min, R., H. Lan, Z. Cao, et al. "A gradually distilled CNN for SAR target recognition," IEEE Access, Vol. 7, 42190-42200, 2019.
doi:10.1109/ACCESS.2019.2906564 Google Scholar

21. Zhao, F. X., J. Du, H. Liu, et al. "Application of deep complex extreme learning machine in radar HRRP target recognition," Telecommunication Engineering, Vol. 61, No. 3, 298-303, 2021. Google Scholar

22. Zhang, X., L. X. Han, R. Mark, et al. "A gans-based deep learning framework for automatic subsurface object recognition from ground penetrating radar data," IEEE Access, 9, 2021. Google Scholar

23. Wang, L. Y., H. F. Tao, C. Xu, et al. "Fault diagnosis of CNN bearing based on multi-layer training interference," Control Engineering of China, Vol. 29, No. 9, 1652-1657, 2022. Google Scholar

24. Lian, X. Q., Z. H. Luo, M. H. Cai, et al. "EEG emotion recognition method based on Convolutional neural network," Computer Simulation, Vol. 39, No. 8, 268-274, 2022. Google Scholar

25. Liu, J., N. Fang, Y. J. Xie, et al. "Distribution characteristics of target dynamic RCS under attitude disturbance," Systems Engineering and Electronics, Vol. 37, No. 4, 775-781, 2015. Google Scholar

26. Wang, C. Y., Y. D. Wu, J. N. Wang, et al. "SAR target recognition based on improved CNN and data enhancement," Systems Engineering and Electronics, Vol. 44, No. 8, 2483-2487, 2022. Google Scholar

27. Li, X. Q., X. C. Zhang, Z. J. Cai, et al. "Research on wine label image data enhancement based on viewpoint transformation," Journal of Signal Processing, Vol. 38, No. 1, 43-54, 2022. Google Scholar

28. Gerdan, D., A. Beyaz, and M. Vatandas, "Classification of apple varieties: Comparison of ensemble learning and naive bayes algorithms in H₂O framework," Journal of Agricultural Faculty of Gaziosmanpasa University, Vol. 37, No. 1, 9-16, 2020. Google Scholar

Ms. Huajuan Zhu

Professor Qiusheng Li