Multifractal correlation, which studies the spatial correlation characteristics of two points with different singularity indexes, is a generalization of multifractal single point statistic. This paper introduces multifractal correlation theory into the characteristic analysis of aircraft echoes from low-resolution surveillance radars, and discusses the application of multifractal correlation characteristics in target classification. Firstly, on basis of introducing multifractal correlation theory, the multifractal correlation characteristics of aircraft echoes from surveillance radars are analyzed in detail by means of the multifractal correlation analysis. Secondly, on basis of the foregoing analysis, several characteristic parameters of the echo multifractal correlation spectrum are defined, and the support vector machine (SVM) based on the defined characteristic parameters is taken as the classifier to classify different types of aircraft targets. Finally, real recorded aircraft echo data are adopted to do the classification experiments, and the experimental results validate the proposed method.
"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. doi:10.2528/PIERB13052202
1. Huang, P. K., H. C. Yin, and X. J. Xu, Radar Target Characteristics, Publishing House of Electronic Industry, Beijing, 2005.
2. Nalecz, M., R. R. Andrianik, and A. Wojtkiewicz, "Micro-Doppler analysis of signal received by FMCW radar," Proceedings of International Radar Symposium, 231-235, 2003.
3. Ding, J. and X. Zhang, "Automatic classification of aircraft based on modulation features," Journal of Tsinghua University (Science and Technology), Vol. 43, No. 7, 887-890, 2003.
4. Chen, V. C., F. Y. Li, S. S. Ho, et al. "Micro-Doppler effect in radar: Phenomenon, model, and simulation study," IEEE Trans. AES, Vol. 42, No. 1, 2-21, 2006.
5. 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.
6. Elshafei, M., S. Akhtar, and M. S. Ahmed, "Parametric models for helicopter identification using ANN," IEEE Transactions on Aerospace and Electronic Systems, Vol. 36, No. 4, 1242-1252, 2000. doi:10.1109/7.892672
7. Melendez, G. J. and S. B. Kesler, "Spectrum estimation by neural networks and their use for target classification by radar," Proceedings of International Conference on Acoustics, Speech, and Signal Processing, 3615-3618, 1995.
8. Moses, R. L. and J. W. Carl, "Autoregressive modeling of radar data with application to target identification," Proceedings of IEEE National Radar Conference, 220-224, 1988.
9. Pellegrini, S. P. F. and C. S. Pardini, "Radar signals analysis oriented to target characterization applied to civilian ATC radar," Proceedings of IEE International Radar Conference, 438-445, 1992.
10. 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
11. 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.
12. 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.
13. 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.
14. Li, Q. S. and W. X. Xie, "Multifractal modeling of aircraft echoes from low-resolution radars," Proceedings of IET International Radar Conference, 2013.
16. Lee, S. J. and T. C. Halsey, Physiea A, Vol. l64, 575, 1990.
17. O'Neil, J. and C. Meneveau, "Spatial correlation in turbulence: Predications from the multifractal formalism and comparison with experiments," Physics Fluids A, Vol. 5, No. 1, 158-172, 1993. doi:10.1063/1.858801
18. Zhou, W., Y.-J. Wang, and Z.-H. Yu, "On the multifractal and multifractal correlation of random binomial measures," Journal of Nonlinear Dynamics in Science and Technology, Vol. 8, No. 3, 199-207, 2001.
19. Shadkhoo, S. and G. R. Jafari, "Multifractal detrended cross-correlation analysis of temporal and spatial seismic data," The European Physical Journal B, Vol. 72, 679-683, 2009. doi:10.1140/epjb/e2009-00402-2
20. Hajian, S. and M. S. Movahed, "Multifractal detrended cross-correlation analysis of sunspot numbers and river flow fluctuations," Physics, Data Analysis, Statistics and Probability, 1-13, Jul. 2010.
21. Guan, J., N. B. Liu, and J. Song, "Multifractal correlation characteristic for radar detecting low-observable target in sea clutter," Signal Processing, Vol. 90, No. 2, 523-535, Elsevier, 2010. doi:10.1016/j.sigpro.2009.07.021
22. Guan, J., N.-B. Liu, J. Zhang, et al. "Multifractal correlation characteristic of real sea clutter and low-observable targets detection," Journal of Electronics and Information Technology, Vol. 32, No. 1, 54-61, 2010. doi:10.3724/SP.J.1146.2008.00980