volume | PIER Journals

Abstract

As a kind of complicated targets, the nonrigid vibration of aircraft, their attitude change, and the rotation of their ro-tating parts will induce complicated nonlinear modulation on their echoes from low-resolution radars. These kinds of modulation play an important role in target classification. However, due to the influence of clutter and noise, these kinds of modulation have the characteristics of fuzziness and randomness. As a quantitative to qualitative conversion model based on traditional probability statis-tics theory and fuzzy theory, backward cloud model can be used to model and analyze the modulation characteristics of the conven-tional low-resolution radar echoes from aircraft targets. By considering the sample values of the echo data as individual cloud drop-lets, the paper extracts the cloud digital features such as the expectation, entropy and hyper-entropy of each group of echo data, and investigates the application of these features in aircraft target classification based on support vector machine. The research results show that the backward cloud model can describe the aircraft echoes well, and the echo cloud digital features can be effectively used for the classification and identification of aircraft targets.

1. Ding, J. J., Target Recognition Technology of Air Defense Radar, National Defense Industry Press, 2008.

2. Huang, P. K., H. C. Yin, and X. J. Xu, Radar Target Characteristics, Publishing House of Electronic Industry, 2005.

3. Li, Q. S., Fractal Theory for Target Recognition with Conventional Radars and its Application, Xidian University Press, 2019.

4. Ding, J. J. and X. D. Zhang, "Automatic classification of aircraft based on modulation features," Journal of Tsinghua University (Science & Technology), Vol. 43, No. 7, 887-890, 2003. Google Scholar

5. Li, Q. S., "Analysis of modulation characteristics on return signals from aircraft rotating blades in the conventional radar," Journal of University of Chinese Academy of Sciences, Vol. 30, No. 6, 829-838, 2013. Google Scholar

6. 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

7. 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

8. Martin, J. and B. Mulgrew, "Analysis of the effects of blade pitch on the radar return signal from rotating aircraft blades," Proceedings of IET International Conference on Radar, 446-449, 1992. Google Scholar

9. Bell, M. R. and R. A. Grubbs, "JEM modeling and measurement for radar target identification," IEEE Transactions on Aerospace and Electronic Systems, Vol. 29, No. 1, 73-87, 1993.
doi:10.1109/7.249114 Google Scholar

10. Pizza, E., "Radar signals analysis and modellization in the presence of JEM application in the civilian ATC radars," IEEE Aerospace and Electronic Systems Magazine, Vol. 14, No. 1, 35-40, 1999.
doi:10.1109/62.738353 Google Scholar

11. Li, Q. S., H. X. Zhang, Y. C. Deng, et al. "Aircraft target classification based on ARMA harmonic retrieval," Journal of Gannan Normal University, Vol. 40, No. 3, 51-56, 2019. Google Scholar

12. 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

13. 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

14. Li, Q. S. and W. X. Xie, "Classification of aircraft targets with low-resolution radars based on multifractal spectrum features," Journal of Electromagnetic Waves and Applications, Vol. 27, No. 16, 2090-2100, 2013.
doi:10.1080/09205071.2013.832394 Google Scholar

15. Du, L., L. S. Li, W. L. Li, et al. "Aircraft target classification based on correlation features from time-domain echoes," Journal of Radars, Vol. 4, No. 6, 621-629, 2015. Google Scholar

16. Yong, Y. W., P. J. Hoon, B. J. Woo, 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

17. Du, L., H. R. Shi, L. S. Li, et al. "Feature extraction method of narrow-band radar airplane signatures based on fractional Fourier transform," Journal of Electronics & Information Technology, Vol. 38, No. 12, 3093-3099, 2016. Google Scholar

18. Li, D., Artificial Intelligence with Uncertainty, 2nd Ed., National Defense Industry Press, 2014.

19. Ji, H. B., "Research on target recognition and classification method by conventional radar,", 43-46, Doctoral Dissertation of Xidian University, 1999. Google Scholar

20. Duda, R. O., P. E. Hart, and D. G. Stork, Pattern Classification, 2nd Ed., 259-264, John Wiley and Sons, 2001.

Professor Qiusheng Li

Dr. Li Wang