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2018-10-30
Research on Analysis of High-Order Fractal Characteristics of Aircraft Echoes and Classification of Targets in Low-Resolution Radars
By
Qiusheng Li,

    Professor Qiusheng Li

    School of Physics and Electronic Information

    Gannan Normal University

    China

    Homepage

Huaxia Zhang,

    Dr. Huaxia Zhang

    School of Physics and Electronic Information

    Gannan Normal University

    China

    Homepage

Rongsheng Lai

    Dr. Rongsheng Lai

    School of Physics and Electronic Information

    Gannan Normal University

    China

    Homepage

Progress In Electromagnetics Research M, Vol. 75, 61-68, 2018
doi:10.2528/PIERM18081101 | Google Scholar

Abstract
High-order fractal characteristics of low-resolution radar echoes provide a supplementary description of the dynamic characteristics of the echo structure of a target, which provides a new way for the classification and recognition of targets with low-resolution radars. On basis of introducing the definition of high-order fractal statistic-lacunarity as well as its calculation method and the lacunarity characteristics of a target echo under additive fractal clutter background, this paper analyzes the characteristics of the lancunarity parameter variation of target echoes from a surveillance radar at a VHF band, and puts forward a classification method for aircraft based on the feature of the echo lacunarity scale change rate from the viewpoint of pattern recognition. The target classification experiments using real recorded echo data show that, as a high-order fractal characteristic parameter, the lacunarity scale change rate can be used as an effective feature for aircraft target classification and recognition, and the proposed method has good classification performance.
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Citation
Qiusheng Li, Huaxia Zhang, and Rongsheng 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.
doi:10.2528/PIERM18081101
References

1. Ding, J. J., Target Recognition Technology of Air Defense Radar, Vol. 40, 44-66, National Defense Industry Press, Beijing, 2008.

2. Li, Q. S., H. X. Zhang, Q. Lu, et al. "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.
doi:10.2528/PIERM18030904        Google Scholar

3. 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

4. Leung, H. and J. Wu, "Bayesian and Dempster-Shafer target identification for radar surveillance," IEEE Transactions on Aerospace & Electronic Systems, Vol. 36, No. 2, 432-447, 2000.
doi:10.1109/7.845221        Google Scholar

5. Selver, M. A., E. Y. Zoral, and M. Secmen, "Real time classification of targets using waveforms in resonance scattering region," Microwave Conference. IEEE, 560-563, 2015.        Google Scholar

6. 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

7. 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

8. Ni, J., S. Y. Zhang, H. F. Miao, et al. "Target classification of low-resolution radar based on fractional Brown feature," Modern Radar, Vol. 33, No. 6, 46-48, 2011.        Google Scholar

9. 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

10. 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.        Google Scholar

11. Ren, Y., Y. Li, and X. Shan, "Aircraft HRRP classification method based on self-similar characteristics of CWT," Journal of Tsinghua University, Vol. 42, No. 7, 873-876, 2002.        Google Scholar

12. Ye, F. and Z. F. Yu, "Recognition of radar emitter signal modulation mode based on multifractal and high-order fractal feature," Ship Electronic Engineering, Vol. 30, No. 4, 116-118, 2010.        Google Scholar

13. Gan, D. and Z. Shouhong, "High-order fractal characterization of sea-scattered signals and detection of sea-surface targets," Electronics Letters, Vol. 35, No. 5, 424-425, 1999.
doi:10.1049/el:19990263        Google Scholar

14. He, S. H., S. Q. Yang, A. G. Shi, et al. "Detection of moving target under sea background based on based on high-order fractal feature," Laser & Infrared, Vol. 38, No. 6, 602-604, 2008.        Google Scholar

15. Guan, J., N. B. Liu, Y. Huang, et al. Fractal Theory for Radar Target Detection and Its Application, Publishing House of Electronics Industry, Beijing, 2011.

16. Mandelbrot, B., The Fractal Geometry of Nature, Revised and Enlarged Edition, New York W.h.freeman & Co.p, 1983.

17. Du, G. and S. H. Zhang, "Radar signal detection based on high-order fractal feature," Acta Electronica Sinica, Vol. 28, No. 3, 90-92, 2000.        Google Scholar

18. Xie, W. L., Q. L. Zhang, Y. H. Chen, et al. "The study of signal detection in clutter by fractal method," Title of paper, book, or conference proceedings, Vol. 21, No. 5, 628-633, 1999.        Google Scholar

19. Yang, Y. H. and Y. Li, "Fractal characteristics of sea clutter by empirical mode decomposition," Journal of Dalian Maritime University, Vol. 43, No. 3, 101-106, 2017.        Google Scholar

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

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