volume | PIER Journals

Abstract

Due to the restrictions of low-resolution radar system and the influence of background clutter during the target detection, it is difficult to classify different kinds of low-resolution radar aircraft targets. In this paper, we propose a multifractal correlation method in the optimal fractional Fourier domain found by fractional Fourier transform (FrFT), in which we extract the multifractal correlation features of aircraft target echoes and do target identification combined with the support vector machine. The experimental results show that FrFT can enhance the multifractal correlation characteristics of aircraft target echoes; the multifractal correlation features extracted from the optimal fractional Fourier domain can effectively distinguish different types of aircraft; and the classification and recognition rates of the multifractal correlation method in the optimal fractional Fourier domain are higher than that of the multifractal correlation method in time domain and the multifractal method in the optimal fractional Fourier domain.

1. Xin, Y. L. and R. C. Xu, "Research on low-resolution radar target recognition method," Modern Electronic Technology, Vol. 18, 17-19+22, 2005. Google Scholar

2. Zhang, G. W., R. D. Li, and D. Wang, "A survey of low resolution radar target classification methods," Digital Communication World, Vol. 5, 280, 2018. Google Scholar

3. Boord, W. J. and J. B. Hoffman, "Air and Missile Defense Systems Engineering," Taylor and Francis, 2016. Google Scholar

4. Wang, F. Y., D. Luo, and W. H. Liu, "Research on aircraft target classification and recognition technology of low resolution airborne radar," Journal of Radar, Vol. 3, No. 4, 444-449, 2014. Google Scholar

5. Yang, S. F., H. K. Wu, X. Wang, et al. "Modulation feature extraction and classification recognition of low resolution radar targets," Electronic Information Countermeasure Technology, Vol. 4, 15-20, 2015. Google Scholar

6. Shao, Y., H. L. Wang, H. J. Zhang, et al. "Low resolution radar target recognition based on waveform characteristics," Shipboard Electronic Countermeasure, Vol. 38, No. 4, 62-65, 2015. Google Scholar

7. Song, X. J., "Research on target classification and recognition of low resolution radar," Radar Science and Technology, Vol. 14, No. 3, 286-290, 2016. Google Scholar

8. Carriere, R. and R. L. Moses, "Autoregressive moving average modeling of radar target signatures," NASA STI/Recon Technical Report N, Vol. 88, 225-229, 1988. Google Scholar

9. Chen, F., H. W. Liu, and B. Z. Du, "Target classification with low-resolution radar based on dispersion situations of eigenvalue spectra," Science China: Information Sciences, Vol. 53, No. 7, 1446-1460, 2010.
doi:10.1007/s11432-010-3099-5 Google Scholar

10. Li, Q. S. and W. X. Xie, "Air defense radar target classification method based on multifractal characteristics," Application Research of Computers, Vol. 30, No. 2, 405-409, 2013. Google Scholar

11. Li, Q., H. Zhang, Q. Lu, and L. Wei, "Research on analysis of aircraft echo characteristics and classification of targets in low-resolution radars based on EEMD," Progress In Electromagnetics Research M, Vol. 68, 61-68, 2018. Google Scholar

12. Li, Q. S., "Analysis of echo modulation characteristics of rotating components of aircraft target on conventional radar," Journal of Chinese Academy of Sciences, Vol. 30, No. 6, 829-838, 2013. Google Scholar

13. Tao, R., L. Qi, and Y. Wang, Principle and Application of Fractional Fourier Transform, Tsinghua University Press, 2004.

14. Xie, D. L., Z. Z. Chen, Y. M. Hong, et al. "Suppression of LFM jamming in HF ground wave radar by fractional Fourier transform," Telecommunication Engineering, Vol. 56, No. 3, 313-318, 2016. Google Scholar

15. Chen, Y., H. C. Zhao, S. Chen, et al. "Missile-borne SAR imaging algorithm based on fractional Fourier transform," Journal of Physics, Vol. 63, No. 10, 358-366, 2014. Google Scholar

16. Elgamel, S. A., C. Clemente, and J. J. Soraghan, "Radar matched filtering using the fractional Fourier transform," IET Sensor Signal Processing for Defence, 2010. Google Scholar

17. Yu, G., S.-C. Piao, and X. Han, "Fractional Fourier transform-based detection and delay time estimation of moving target in strong reverberation environment," IET Radar, Sonar and Navigation, Vol. 11, No. 8, 1367-1372, 2017.
doi:10.1049/iet-rsn.2016.0601 Google Scholar

18. Du, L., H. R. Shi, L. S. Li, et al. "Feature extraction method of narrowband radar aircraft target echo based on fractional-order Fourier transform," Journal of Electronics and Information, Vol. 38, No. 12, 3093-3099, 2016. Google Scholar

19. Jing, D., H. Cha, L. Zuo, et al. "Multifractal elimination trend fluctuation analysis of sea clutter," Journal of Naval Engineering University, Vol. 29, No. 5, 29-33, 2017. Google Scholar

20. Li, Q., W. Xie, and C. Luo, "Identification of aircraft targets based on multifractal spectrum features," 2012 11th International Conference on Signal Processing, ICSP 2012, 1821-1824, Institute of Electrical and Electronics Engineers Inc., Beijing, China, October 21–25, 2012. Google Scholar

21. Fan, Y. F., F. Luo, M. Li, et al. "Fractal characteristics of AR spectrum expansion of sea clutter and detection of weak targets," Journal of Xi’an University of Electronic Science and Technology (Natural Science Edition), Vol. 44, No. 1, 59-64, 2017. Google Scholar

22. Fan, Y. F., F. Luo, M. Li, et al. "Multifractal characteristics of AR spectrum of sea clutter and weak target detection method," Journal of Electronic and Information Science, Vol. 38, No. 2, 455-463, 2016. Google Scholar

23. Qu, Z. Y., X. J. Mao, and C. B. Hou, "Radar signal recognition based on singular value entropy and fractal dimension," Systems Engineering and Electronic Technology, Vol. 40, No. 2, 303-307, 2018. Google Scholar

24. Li, Q. S., X. Y. Liu, and J. P. Chen, "Fractal modeling and target classification of conventional radar aircraft echoes," Journal of Gannan Normal University, Vol. 36, No. 3, 34-39, 2015. Google Scholar

25. Li, Q. S., X. D. Yuan, and L. X. Guan, "Multifractal analysis of aircraft target echo from conventional radar," Journal of Anhui University (Natural Science Edition), Vol. 36, No. 5, 47-54, 2012. Google Scholar

26. Li, Q., X. Xie, and Q. Lu, "Generalized dimension spectrum features based classification method for aircraft," 2016 CIE International Conference on Radar, RADAR 2016, Institute of Electrical and Electronics Engineers Inc., Guangzhou, China, October 10–13, 2016. Google Scholar

27. Li, Q. S., J. H. Pei, and X. Y. Liu, "Self-affine fractal modelling of aircraft echoes from low-resolution radars," Defence Science Journal, Vol. 66, No. 2, 151-155, 2016.
doi:10.14429/dsj.66.8423 Google Scholar

28. Li, Q. S., W. X. Xie, and J. X. Huang, "Extended fractal characteristic analysis and target classification of air defense radar aircraft echo," Signal Processing, Vol. 29, No. 8, 1091-1097, 2013. Google Scholar

29. Li, Q., H. Zhang, and R. Lai, "Research on analysis of high-order fractal characteristics of aircraft echoes and classification of targets in low-resolution radars," Progress In Electromagnetics Research M, Vol. 75, 61-68, 2018. Google Scholar

30. Li, Q. and W. Xie, "Research on analysis of multifractal correlation characteristics of aircraft echoes and classification of targets in surveillance radars," Progress In Electromagnetics Research B, Vol. 54, 27-44, 2013. Google Scholar

31. Guan, J., N. B. Liu, J. Zhang, et al. "Multi-fractal correlation characteristics of sea clutter and weak target detection," Journal of Electronic and Information Science, Vol. 32, No. 1, 54-61, 2010.
doi:10.3724/SP.J.1146.2008.00980 Google Scholar

32. Gu, Z. M., X. G. Zhang, and Q. Wang, "Multifractal characteristics of sea clutter and target detection in FRFT domain," Journal of Nanjing University (Natural Science), Vol. 53, No. 4, 731-737, 2017. Google Scholar

33. Li, Q. S., X. C. Xie, H. Zhu, et al. "Fractal characteristic analysis and target classification of low resolution radar aircraft echoes in fractional fourier domain," Application Research of Computers, Vol. 35, No. 8, 315-318+322, 2018. Google Scholar

34. Zhang, H., Q. Li, C. Rong, and X. Yuan, "Target classification with low-resolution radars based on multifractal features in fractional fourier domain," Progress In Electromagnetics Research M, Vol. 79, 51-60, 2019.
doi:10.2528/PIERM18110503 Google Scholar

35. Williams, W. J., M. L. Brown, and A. O. H. Iii, "Uncertainty, information, and time-frequency distributions," Proceedings of SPIE — The International Society for Optical Engineering, Vol. 1566, 144-156, 1991. Google Scholar

36. Chen, Z. R., H. Gu, W. M. Su, et al. "Improved support vector machine for low resolution radar target classification," Systems Engineering and Electronic Technology, Vol. 39, No. 10, 2456-2462, 2017. Google Scholar

Dr. Huaxia Zhang

Professor Qiusheng Li