volume | PIER Journals

Abstract

Due to the limitations of low-resolution radar system and background clutter, the task of target classification with conventional low-resolution radars is relatively difficult. This paper introduces fractional Fourier transform (FrFT) to process aircraft echoes in order to find the optimal fractional Fourier domain, in which signal to noise ratio can reach the maximum, and then applies multifractal theory to the feature extraction of radar targets. Based on the above, we use SVM to do target classification. Experiments show that the multifractal characteristics of aircraft echoes can be enhanced by FrFT, and the features extracted from the optimal fractional Fourier domain can be used effectively to classify different types of aircraft even in the case of low SNR.

1. Li, Q. S., W. X. Xie, and J. X. Huang, "Extended fractal characteristic analysis and target classification study of surveillance radar echoes," Journal of Signal Processing, Vol. 29, No. 9, 1091-1097, 2013. Google Scholar

2. Yong, Y. W., P. J. Hoon, B. J. Wu, et al. "Automatic feature extraction from jet engine modulation signals based on an image processing method," IET Radar, Sonar & Navigation, Vol. 9, No. 7, 783-789, 2015.
doi:10.1049/iet-rsn.2014.0281 Google Scholar

3. Yang, S. F., H. K. Wu, X. Wang, et al. "Target feature extraction and recognition based on low-resolution radar," Electronic Information Countermeasures Technology, Vol. 30, No. 4, 15-20, 2015. Google Scholar

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

5. Huang, P. K., H. C. Yin, and X. J. Xu, Radar Target Characteristics, 114-115, Beijing Electronic Industry Press, 2005.

6. Yun, S., H. L. Wang, H. J. Zhang, et al. "Target recognition of low resolution radar based on waveform feature," Shipboard Electronic Countermeasure, Vol. 38, No. 4, 62-69, 2015. Google Scholar

7. Chen, H., Y. Ding, X. H. Jiao, et al. "Target recognition for low resolution radar based on generalized fuzzy function," Shipboard Electronic Countermeasure, Vol. 39, No. 5, 46-49, 2016. Google Scholar

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

9. 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

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

11. 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 & Navigation, Vol. 11, No. 9, 1367-1372, 2017.
doi:10.1049/iet-rsn.2016.0601 Google Scholar

12. Elgamel, S. A. and J. Soraghan, "A new fractional Fourier transform based monopulse tracking radar processor," 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2010. Google Scholar

13. Xian, M., Z. W. Zhuang, S. P. Xiao, et al. "Chaotic multifractal analysis and identification of radar target echoes," Journal of National University of Defense Technology, Vol. 2, 63-67, 1998. Google Scholar

14. Shi, Z. G., J. X. Zhou, H. Z. Zhao, et al. "Multifractal analysis of sea clutter," Data Acquisition and Processing, Vol. 2, 168-173, 2006. Google Scholar

15. Li, Q. S., X. Y. Liu, and J. P. Chen, "Research on fractal modeling of aircraft echoes and classification of targets in conventional radars," Journal of Gannan Normal University, Vol. 36, No. 3, 34-39, 2015. Google Scholar

16. Li, Q. S., X. D. Yuan, and L. X. Guan, "Analysis of the multifractal characteristics of the conventional radar return signal from aircraft targets," Journal of Anhui University (Natural Science Edition), Vol. 36, No. 5, 47-54, 2012. Google Scholar

17. Li, Q. S. and W. X. Xie, "Target classification with low-resolution surveillance radars based on multifractal features," Computer Application Research, Vol. 30, No. 2, 405-409, 2013. Google Scholar

18. Li, Q. S., X. C. Xie, H. Zhu, et al. "Fractal characteristic analysis and target classification of aircraft echoes of low-resolution radars in fractional Fourier domain," Application Research of Computers, Vol. 35, No. 9, 2869-2872, 2018. Google Scholar

19. Ozaktas, H. M., O. Arikan, M. A. Kutay, et al. "Digital computation of the fractional Fourier transform," IEEE Transactions on Signal Processing, Vol. 44, No. 9, 2141-2150, 1996.
doi:10.1109/78.536672 Google Scholar

20. Williams, W. J., "Uncertainty, information, and time-frequency distributions," Proceedings of SPIE, The International Society for Optical Engineering, Vol. 1566, 144-156, 1991. Google Scholar

21. Hao, B. and M. Luo, "Performance analysis and interference suppression method of GPS system under time-varying single-tone interference," Computer Application Research, Vol. 35, No. 321(7), 185-188, 2018. Google Scholar

22. Ouyang, X. and M. G. Amin, "Short-time Fourier transform receiver for nonstationary interference excision in direct sequence spread spectrum communications," IEEE Transactions on Signal Processing, Vol. 49, No. 4, 851-863, 2001.
doi:10.1109/78.912929 Google Scholar

Dr. Huaxia Zhang

Professor Qiusheng Li

Dr. Chuicai Rong

Dr. Xindi Yuan