2013-02-06
Classification of Aircraft Targets with Surveillance Radars Based on Fuzzy Fractal Features
By
Progress In Electromagnetics Research M, Vol. 29, 65-77, 2013
Abstract
The fuzzy fractal characteristics of return signals from aircraft targets in conventional radars offer a description of dynamic features 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 fuzzy fractal theory, the paper analyzes the fuzzy fractal characteristics of return signals from aircraft targets in a VHF-band surveillance radar by means of the fuzzy fractal analysis, and puts forward a fuzzy-fractal-feature-based classification method for aircraft targets with a low-resolution radar from the viewpoint of pattern recognition. The analysis shows that the fuzzy fractal characteristic parameters such as the local fuzzy fractal dimension (LFFD) and local degree of fractality (LGF) can be used as effective features for aircraft target classification and recognition. The results of classification experiments validate the proposed method.
Citation
Qiusheng Li, and Weixin 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
References

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

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

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

4. Chan, , S. C. and K. C. Lee, "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

5. Lin, Q. S., W. D. Hu, H. Yu, et al. "A study of target classification method based on low-resolution radar return sequences image profile," Modern Radar, Vol. 27, No. 3, 24-28, 2005.        Google Scholar

6. Chen, W. T., C. R. Xu, and Z. P. Chen, "Low-resolution radar target recognition based on gray-level map features," Modern Radar, Vol. 28, No. 9, 48-50, 2006.        Google Scholar

7. Leung, H. and J. F. Wu, "Bayesian and Dempster-Shafer target identification for radar surveillance," IEEE Transactions on Aerospace and Electronic Systems, Vol. 36, No. 2, 432-447, 2000.        Google Scholar

8. Zhang, H. H., W. Wang, and W. D. Jiang, "Aircraft target classification based on registration information for low-resolution radar," Systems Engineering and Electronics,, Vol. 26, No. 4, 488-490, 2004.        Google Scholar

9. 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

10. 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

11. 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

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

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

14. 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 Radar Conference,, 446-449, 1992.        Google Scholar

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

16. 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

17. Chen, V. C., F. Y. Li, S. S. Ho, et al. "Micro-Doppler effect in radar: Phenomenon, model, and simulation study," IEEE Transactions on Aerospace and Electronic Systems, Vol. 42, No. 1, 2-21, 2006.        Google Scholar

18. Zhuang, Z. W., Y. X. Liu, and X. Li, "The achievements of target characteristic with micro-motion," Acta Electronica Sinica, Vol. 35, No. 3, 520-525, 2007.        Google Scholar

19. 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

20. Li, Q. S., W. X. Xie, and C. Luo, "Identification of aircraft targets based on multifractal spectrum features," Proceedings of IEEE International Conference on Signal Processing, 1821-1824, 2012.        Google Scholar

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

22. Kamijo, K. and A. Yamanouchi, "Signal processing using fuzzy fractal dimension and grade of fractality-Application to fluctuations in seawater temperature," Proceedings of IEEE Symposium on Computational Intelligence in Image and Signal Processing, 133-138, 2007.        Google Scholar

23. "Time series analysis for altitude structure using local fractal dimension --- An example of seawater temperature fluctuation around Izu Peninsula,", Technical Report of IEICE, NLP2004-3, 2004.        Google Scholar

24. Ding, J. J. and X. D. Zhang, "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

25. 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

26. 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, 3615-3618, 1995.        Google Scholar

27. 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

28. 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

29. 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

30. Jahangir, M., K. M. Pointing, and J. W. O'Loghlen, "A robust Doppler classi¯cation technique based on hidden Markov models," Proceedings of IEEE International Conference on Radar,, Vol. 162, No. 166, 2002.        Google Scholar

31. Jahangir, M., K. M. Pointing, 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, No. 5, 2003.        Google Scholar

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

33. 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, 1390-1392, 2005.        Google Scholar

34. Dullard, , 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

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