volume | PIER Journals

Abstract

As a kind of complex targets, the non-rigid vibration of an aircraft as well as its attitude change and the rotation of its rotating parts will induce complex nonlinear modulation on its echo from low-resolution radars. If these nonlinear modulation features which reflect the physical characteristics of an aircraft target can be extracted effectively, then they are helpful to target classification and recognition. However, the echo translational component and background clutter have a very adverse effect on the extraction of such features. On basis of introducing the ensemble empirical mode decomposition (EEMD) algorithm, the paper firstly performs the decomposition of real recorded aircraft echo data from a low-resolution radar by EEMD and distinguishes the false component, body translational component and micro-motion component by calculating waveform entropy in the Doppler domain. Secondly, it carries out characteristic analysis and feature extraction further on the echo micro-motion component separated and extracts three features of the micro-motion component, including Doppler domain waveform entropy E_mc, normalized equivalent Doppler spectrum width BW₀, and normalized frequency interval between the adjacent maximum spectral peaks on both sides of the spectrum center Δf₀. The analysis results show that EEMD can be used to separate the body translational component and micromotion component of an aircraft echo effectively, and the proposed features (E_mc, BW₀ and Δf₀) can be used as effective features for aircraft target classification and recognition.

1. Ding, J. J., Target Recognition Technology of Air Defense Radar, National Defense Industry Press, Beijing, 2005.

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

3. Nalecz, M., R. R. Andrianik, and A. Wojtkiewicz, "Micro-Doppler analysis of signal received by FMCW radar," Proceedings of International Radar Symposium, 231-235, 2003.

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.

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.

6. Molchanov, P., K. Egiazarian, J. Astola, et al. "Classification of aircraft using micro-Doppler bicoherence-based features," IEEE Transactions on Aerospace & Electronic Systems, Vol. 50, No. 2, 1455-1467, 2014.
doi:10.1109/TAES.2014.120266

7. Chen, Y. B., S. D. Li, and J. Yang, "Research on micro-motion modulation characteristics of multi-rotor aircraft’s rotors," Proceedings of CIE International Conference on Radar, 1-4, 2016.

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.

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

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

11. Ding, J. J., X. D. Zhang, and J. J. Lv, "Modeling of the modulation signature for aircraft returns in the conventional radar," Systems Engineering and Electronics, Vol. 25, No. 11, 1407-1410, 2003.

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

13. Li, Q. S., X. C. Xie, H. Zhu, et al. "Fractal characteristic analysis of aircraft echoes from low-resolution radars and target classification in fractional Fourier domain,", Vol. 35, No. 10, [2017-09-27], 2018, http://www.arocmag.com/article/02-2018-10-016.html.

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

15. Li, Q. S. and W. X. 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.

16. Li, Q. S. and W. X. Xie, "Classification of aircraft targets with surveillance radars based on fuzzy fractal features," Progress In Electromagnetics Research M, Vol. 29, 65-77, 2013.
doi:10.2528/PIERM12121601

17. Li, Q. S., W. X. Xie, and J. X. Huang, "Research on analysis of extending fractal characteristics of aircraft echoes and classification of targets in surveillance radars," Journal of Signal Processing, Vol. 29, No. 9, 1091-1097, 2013.

18. 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.

19. Wu, Z. H. and N. E. Huang, "Ensemble empirical mode decomposition: A noise-assisted data analysis method," Advances in Adaptive Data Analysis, Vol. 1, No. 1, 0900004, 2011.

20. Huang, N. E., Z. Shen, S. R. Long, et al. "The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis," Proceedings Mathematical Physical & Engineering Sciences, Vol. 454, No. 1971, 903-995, 1998.
doi:10.1098/rspa.1998.0193

21. Qin, N. P., Jiang, Y. K. Sun, et al. "One-dimensional HRRP detection technique for radar target based on waveform entropy," Journal of Vibration, Measurement & Diagnosis, Vol. 35, No. 5, 885-891, 2015.

22. Tang, B., Y. Zhang, and J. Tang, "An efficient minorization maximization approach for MIMO radar waveform optimization via relative entropy," IEEE Transactions on Signal Processing, 400-411, 2017.

23. 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

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

Professor Qiusheng Li

Dr. Huaxia Zhang

Dr. Qing Lu

Dr. Lehui Wei