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.
"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
1. Shirman, Y. D., "Computer Simulation of Aerial Target Radar Scattering, Recognition, Detection, and Tracking," Artech House, Boston, 111-124, 2002.
2. Ding, J. J., "Target Recognition Techniques of Surveillance Radar," National Defense Industry Press, 40-41, 2008.
3. Ghadaki, H. and R. Dizaji, "Target track classification for airport surveillance radar (ASR)," Proceedings of IEEE Conference on Radar,, 24-27, 2006.
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.
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.
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.
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.
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.
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.
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.
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.
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.
13. Yang, S. and S. Yeh, "Electromagnetic backscattering from aircraft propeller blades," EEE Transactions on Magnetics, Vol. 33, 1432-1435, 1997.
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.
15. Yoon, S., B. Kim, and Y. Kim, "Helicopter classification using time-frequency analysis," Electronics Letters, Vol. 36, 1871-1872, 2000.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
35. Duda, R. O., P. E. Hart, and D. G. Stork, Pattern Classification, 2nd Ed., 259-264, John Wiley and Sons, New York, 2001.