Search search
Publication Date
to
Vol. 45
Latest Volume
All Volumes
PIERB 118 PIERB 117 PIERB 116 PIERB 115 PIERB 114 PIERB 113 PIERB 112 PIERB 111 PIERB 110 PIERB 109 PIERB 108 PIERB 107 PIERB 106 PIERB 105 PIERB 104 PIERB 103 PIERB 102 PIERB 101 PIERB 100 PIERB 99 PIERB 98 PIERB 97 PIERB 96 PIERB 95 PIERB 94 PIERB 93 PIERB 92 PIERB 91 PIERB 90 PIERB 89 PIERB 88 PIERB 87 PIERB 86 PIERB 85 PIERB 84 PIERB 83 PIERB 82 PIERB 81 PIERB 80 PIERB 79 PIERB 78 PIERB 77 PIERB 76 PIERB 75 PIERB 74 PIERB 73 PIERB 72 PIERB 71 PIERB 70 PIERB 69 PIERB 68 PIERB 67 PIERB 66 PIERB 65 PIERB 64 PIERB 63 PIERB 62 PIERB 61 PIERB 60 PIERB 59 PIERB 58 PIERB 57 PIERB 56 PIERB 55 PIERB 54 PIERB 53 PIERB 52 PIERB 51 PIERB 50 PIERB 49 PIERB 48 PIERB 47 PIERB 46 PIERB 45 PIERB 44 PIERB 43 PIERB 42 PIERB 41 PIERB 40 PIERB 39 PIERB 38 PIERB 37 PIERB 36 PIERB 35 PIERB 34 PIERB 33 PIERB 32 PIERB 31 PIERB 30 PIERB 29 PIERB 28 PIERB 27 PIERB 26 PIERB 25 PIERB 24 PIERB 23 PIERB 22 PIERB 21 PIERB 20 PIERB 19 PIERB 18 PIERB 17 PIERB 16 PIERB 15 PIERB 14 PIERB 13 PIERB 12 PIERB 11 PIERB 10 PIERB 9 PIERB 8 PIERB 7 PIERB 6 PIERB 5 PIERB 4 PIERB 3 PIERB 2 PIERB 1
All Journals
PIER PIER B PIER C PIER M PIER Letters PIER Photonics
2012-11-01
Target Classification with Low-Resolution Surveillance Radars Based on Multifractal Features
By
Qiusheng Li,

    Professor Qiusheng Li

    School of Physics and Electronic Information

    Gannan Normal University

    China

    Homepage

Weixin Xie

    Dr. Weixin Xie

    Shenzhen University

    China

    Homepage

Progress In Electromagnetics Research B, Vol. 45, 291-308, 2012
doi:10.2528/PIERB12091509 | Google Scholar

Abstract
The multifractal characteristics of return signals from aircraft targets in conventional radars offer a fine description of dynamic characteristics which induce the targets' echo structure; therefore they can provide a new way for aircraft target classification and recognition with low-resolution surveillance radars. On basis of introducing the mathematical model of return signals from aircraft targets in conventional radars, the paper analyzes the multifractal characteristics of the return signals as well as the extraction method of their multifractal features by means of the multifractal analysis of measures, and puts forward a multifractal-feature-based classification method for three types of aircraft targets (including jet aircrafts, propeller aircrafts and helicopters) from the viewpoint of pattern classification. The analysis shows that the conventional radar return signals from the three types of aircraft targets have significantly different multifractal characteristics, and the defined characteristic parameters can be used as effective features for aircraft target classification and recognition. The results of classification experiments validate the proposed method.
Download Full Article (873) View Full Article volume
Citation
Qiusheng Li, and Weixin Xie, "Target Classification with Low-Resolution Surveillance Radars Based on Multifractal Features," Progress In Electromagnetics Research B, Vol. 45, 291-308, 2012.
doi:10.2528/PIERB12091509
References

1. Shirman, Y. D., Computer Simulation of Aerial Target Radar Scattering, Recognition, Detection, and Tracking, 111-124, Artech House, Boston, 2002.

2. Ding, J. J., Target Recognition Techniques of Surveillance Radar, 40-41, National Defense Industry Press, Beijing, 2008.

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.        Google Scholar

4. Ghadaki, H. and R. Dizaji, "Target track classification for airport surveillance radar (ASR)," Proceedings of IEEE Conference on Radar, 24-27, 2006.        Google Scholar

5. Leung, H., "Intelligent radar recognition system for surveillance," Proceedings of IEEE International Conference on Systems, Man and Cybernetics, 2280-2285, 1995.        Google Scholar

6. Leung, H. and J. Wu, "Bayesian and Dempster-Shafer target identification for radar surveillance," IEEE Transactions on erospace and Electronic Systems, Vol. 36, 432-447, 2000.        Google Scholar

7. Chan, S. C., K. C. Lee, and , "Radar target identification by kernel principal component analysis on RCS," Journal of Electromagnetic Waves and Applications, Vol. 26, No. 1, 64-74, 2012.        Google Scholar

8. Pouliguen, P., L. Lucas, F. Muller, et al. "Calculation and analysis of electromagnetic scattering by helicopter rotating blades," IEEE Transactions on Antennas and Propagation, Vol. 50, 1193-1408, 2002.        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, 73-87, 1993.        Google Scholar

10. Piazza, E., "Radar signals analysis and modellization presence of JEM application in the civilian ATC radars," IEEE Aerospace and Electronic Systems Magazine, Vol. 14, 35-40, 1999.        Google Scholar

11. Martin, J., B. Mulgrew, "Analysis of the theoretical return signal from aircraft blades," Proceedings of IEEE International Conference on Radar, 569-572, 1990.        Google Scholar

12. Yang, S. Y. and S. M. Yeh, "Electromagnetic backscattering from aircraft propeller blades," EEE Transactions on Magnetics, Vol. 33, 1432-1435, 1997.        Google Scholar

13. 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

14. Yoon, S., B. Kim, and Y. Kim, "Helicopter classification using time-frequency analysis," Electronics Letters, Vol. 36, 1871-1872, 2000.        Google Scholar

15. Xian, M., Z. W. Zhuang, Z. P. Chen, et al. "The fractal characteristic of radar target based on polarimetry," Proceedings of the IEEE 1996 National Aerospace and Electronics Conference, Vol. 1, 339-344, 1996.        Google Scholar

16. Mishra, A. K., H. Feng, and B. Mulgrew, "Fractal feature based radar signal classification," Proceedings of IET International Conference on Radar Systems, 1-4, 2007.        Google Scholar

17. Ding, J. J., X. D. Zhang, and , "Studies of analysis of JEM signatures and classification of targets in the conventional radar," Journal of Electronics and Information Technology, Vol. 25 , 956-962, 2003.        Google Scholar

18. Elshafei, M., S. Akhtar, and M. S. Ahmed, "Parametric models for helicopter identification using ANN," IEEE Transactions on Aerospace and Electronic Systems, Vol. 36, 1242-1252, 2000.        Google Scholar

19. Melendez, G. J. and S. B. Kesler, "Spectrum estimation by neural networks and their use for target classification by radar," Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 3615-361, 1995.        Google Scholar

20. Moses, R. L. and J. W. Carl, "Autoregressive modeling of radar data with application to target identification," Proceedings of the 1988 IEEE National Radar Conference, 220-224, 1988.        Google Scholar

21. Pellegrini, S. P. F. and C. S. Pardini, "Radar signals analysis oriented to target characterization applied to civilian ATC radar," Proceedings of IET International Conference Radar, 438-445, 1992.        Google Scholar

22. Stove, A., "A Doppler-based target classifier using linear discriminants and principal components," Proceedings of IET Seminar on High Resolution Imaging and Target Classification, 171-176, 2006.        Google Scholar

23. Jahangir, M., K. M. Ponting, and J. W. O'Loghlen, "Robust Doppler classi¯cation technique based on hidden Markov models," Proceedings of IEEE International Conference on Radar, 162-166, 2002.        Google Scholar

24. Jahangir, M., K. M. Ponting, and J. W. O'Loghlen, "Correction to robust Doppler classification technique based on hidden Markov models," Proceedings of IEE International Conference on Radar, Sonar and Navigation, Vol. 150, 387, 2003.        Google Scholar

25. Ji, H. B., J. Li, and W. X. Xie, "Bispectrum based radar target classification," Proceedings of IEEE International Conference on Signal Processing, 419-422, 1998.        Google Scholar

26. Grassberger, P., "Generalized dimensions of strange attractors," Physics Letters A, Vol. 97, 227-230, 1983.        Google Scholar

27. Hentschel, H. G. E. and I. Procaccia, "The infinite number of generalized dimensions of fractals and strange attractors," Physica D, Vol. 8, 435-444, 1983.        Google Scholar

28. Halsey, T. C., M. H. Jensen, et al. "Fractal measures and their singularities: The characterization of strange sets," Physical Review A, Vol. 33, 1141-1151, 1986.        Google Scholar

29. Telesca, L., V. Lapenna, and M. Macchiato, "Mono- and multi-fractal investigation of scaling properties in temporal patterns of seismic sequences," Chaos, Solitons and Fractals, Vol. 19, 1-15, 2004.        Google Scholar

30. Andric, M., Z. Durovic, and B. Zrnic, "Ground surveillance radar target classification based on fuzzy logic approach," Proceedings of IEEE International Conference on Computer as a Tool, Vol. 2, 1390-1392, 2005.        Google Scholar

31. ullard, B. D. and P. C. Dowdy, "Pulse Doppler signature of a rotary wing aircraft," IEEE Aerospace and Electronic Systems Magazine, Vol. 36, 28-30, 1991.        Google Scholar

32. Yoon, S., B. Kim, and Y. Kim, "Helicopter classification using time-frequency analysis," Electronics Letters, Vol. 36, 1871-1872, 2000.        Google Scholar

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

Download Full Article (873) View Full Article volume


The EM Academy piers.org jpier.org Who's Who in EM
Copyright © 2022 The Electromagnetics Academy. All Rights Reserved.