Vol. 99

Front:[PDF file] Back:[PDF file]
Latest Volume
All Volumes
All Issues

Aircraft Target Classification Method Based on EEMD and Multifractal

By Junyong Hu, Qiusheng Li, Qianli Zhang, and Yingjie Zhong
Progress In Electromagnetics Research M, Vol. 99, 223-231, 2021


Due to the limitation of low-resolution radar system and the influence of background clutter in the detection process, it is hard for low-resolution radars to classify and identify aircraft targets. To solve the above problems, a classification method for aircraft based on Ensemble Empirical Mode Decomposition (EEMD) and multifractal is proposed, in which the intrinsic modes are obtained by EEMD, and the waveform entropy in the Doppler domain is used to screen and reconstruct the intrinsic modes. The multifractal feature of the target echo data is extracted from the reconstructed signal, and then the aircraft target classification and recognition experiment is carried out with support vector machine. The experimental results show that the feature data extracted by ensemble empirical mode decomposition and multifractal analysis can be used for the classification and identification of civil aircraft and fighter aircraft, and the accuracy rate is about 98.5%, which is higher than that of time-domain multifractal method.


Junyong Hu, Qiusheng Li, Qianli Zhang, and Yingjie Zhong, "Aircraft Target Classification Method Based on EEMD and Multifractal," Progress In Electromagnetics Research M, Vol. 99, 223-231, 2021.


    1. Li, T., et al., "High-frequency radar aircraft detection method based on neural networks and time-frequency algorithm," IET Radar Sonar Navig., Vol. 7, No. 8, 875-880, 2013.

    2. Weinberg, G. V., "Assessing Pareto fit to high-resolution high-grazing angle sea clutter," IET Electron. Lett., Vol. 47, 516-517, 2011.

    3. Liu, J., N. Fang, B. F. Wang, and Y. J. Xie, "Scale-space theory-based multi-scale features for aircraft classification using HRRP," Electron. Lett., Vol. 52, 475-477, 2016.

    4. Lei, S., X. Qiu, Y. Zhang, L. Huang, and D. Chibiao, "Analysis of the multipath scattering effects in high-resolution SAR images," IEEE Geosci. Remote Sens. Lett., Vol. 17, No. 4, 616-620, Apr. 2020.

    5. Zhang, G., R. Li, and D. Wang, "A review of target classification methods for low-resolution radar," Digital Communication World, Vol. 161, No. 5, 288, 2018.

    6. 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, Jan. 1993.

    7. Li, Q., "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.

    8. Shao, Y., H. Wang, H. Zhang, and H. Chen, "Target recognition of low-resolution radar based on waveform feature," Shipboard Electronic Countermeasure, Vol. 38, No. 4, 62-65+69, 2015.

    9. Zhu, Z. and J. Zhou, "Super-resolution reconstruction of synthetic-aperture radar image using adaptive-threshold singular value decomposition technique," J. Cent. South Univ. Technol., Vol. 18, 809-815, 2011.

    10. Li, F., D. Hu, C. Ding, and W. Zhang, "InSAR phase noise reduction based on empirical mode decomposition," IEEE Geoscience and Remote Sensing Letters, Vol. 10, 1180-1184, 2013.

    11. Xue, W., X. Dai, J. Zhu, Y. Luo, and Y. Yang, "A noise suppression method of ground penetrating radar based on EEMD and permutation entropy," IEEE Geoscience and Remote Sensing Letters, Vol. 16, No. 10, 1625-1629, Oct. 2019.

    12. Pouraimis, G., A. Kotopoulis, E. Kallitsis, and P. Frangos, "Characterization of three-dimensional rough fractal surfaces from backscattered radar data," Elektronika Ir Elektrotechnika, Vol. 23, No. 4, 45-50, 2017.

    13. Azzaz, N. and B. Haddad, "Classification of radar echoes using fractal geometry," Chaos, Solitons & Fractals, Vol. 98, 130-144, 2017.

    14. Li, Q. S., J. H. Pei, and X. Y. Liu, "Self-a±ne fractal modelling of aircraft echoes from low-resolution radars," Defence Science Journal, Vol. 66, No. 2, 151-155, 2016.

    15. Cherouat, S., et al., "Using fractal dimension to target detection in bistatic SAR data," Signal Image & Video Processing, Vol. 9, No. 2, 365-371, 2015.

    16. Li, Q. and W. Xie, "Target classification with low-resolution surveillance radars based on multifractal features," Progress In Electromagnetics Research B, Vol. 45, 291-308, 2012.

    17. Fan, Y., "Study on weak ``Target detection based on fractal and the multifractal analysis in sea clutter background'',", Master's Degree Thesis of Xidian University, 2016.

    18. Huang, N. E., 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.

    19. Wu, Z. and N. E. Huang, "Ensemble empirical mode decomposition: a noise-assisted data analysis method," Advances in Adaptive Data Analysis, Vol. 1, No. 1, 1-41, 2009.

    20. Li, M., J. Wu, L. Zuo, W. Song, and H. Liu, "Aircraft target classification and recognition algorithm based on measured data," Journal of Electronics & Information Technology, Vol. 40, No. 11, 2606-2613, 2018.

    21. Mandelbrot, B. B., The Fractal Geometry of Nature, Freeman, California, 1982.

    22. Grassberger, P., "Generalized dimensions of strange attractors," Physics Letters A, Vol. 97, No. 6, 227-230, 1983.

    23. Hasey, T. C., et al., "Fractal measures and their singularities: The characterization of strange sets," Physics Review A, Vol. 33, No. 2, 1141-1151, 1986.

    24. Hentschel, H. G. E. and I. Procaccia, "The infinite number of generalized dimensions of fractals and strange attractors," Physica D, Vol. 8, No. 3, 435-444, 1983.

    25. Li, Q. S. and W. X. Xie, "Target classification by surveillance radar based on multifractal features," Application Research of Computers, Vol. 30, No. 2, 405-409, 2013.

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

    27. Wang, Z., "Research on text classification based on SVM algorithm,", Master's Degree Thesis of Jilin University, 2017.

    28. Zhang, D., J. Zhang, K. Yao, M. Cheng, and Y. Wu, "Infrared ship-target recognition based on SVM classification," Infrared and Laser Engineering, Vol. 45, No. 1, 179-184, 2016.

    29. Geng, R., D. Cui, and B. Xu, "Support vector machine-based combinational model for air traffic forecasts," J. Tsinghua Univ. (Sci.&Tech.), Vol. 7, 1205-1208, 2008.

    30. Wu, S., Q. Li, and H. Zhu, "Self-affine fractal analysis and target classification of aircraft echoes," Journal of Gannan Normal University, Vol. 37, No. 6, 45-49, 2016.

    31. Li, Q., X. Xie, H. Zhu, and Q. Wu, "Fractal characteristics analysis and target classification of low-resolution radar aircraft echoes using fractional order fourier domain," Application Research of Computers, Vol. 35, No. 9, 2869-2872+2876, 2008.