The research goal of low-resolution radar aircraft target classification is to analyze the category of the given low-resolution radar aircraft target echo. In existing solutions, the feature extraction methods based on rotating modulation spectrum have good performance, such as the complex cepstrum method, autocorrelation method, cycle diagram method, autoregressive model power spectrum method, and singular value decomposition method. Most of these methods are more complicated in calculations, and practical applications often require higher pulse frequencies and longer observation times, which are greatly restricted. In this paper, a classification method based on ensemble empirical mode decomposition and multifractal correlation (CMEEMDMFC) is proposed. The basic design idea is to obtain the intrinsic mode functions (IMFs) by using the signal decomposition ability of ensemble empirical mode decomposition (EEMD) and select some components which are beneficial for improving the signal-to-noise ratio (SNR) for recombination. Then extract the corresponding multifractal correlation (MFC) features from the new signals for recognition. For verifying the validity of the model, a comparison model was selected to test on the same data set. Experimental results show that the proposed model performs well in classification accuracy.
2. Yang, X., "Building detection from high-resolution polarimetric SAR images,", University of Electronic Science and Technology of China, 2017.
3. Zhang, G., R. Li, and D. Wang, "A review of low-resolution radar target classification methods," Digital Communication World, Vol. 5, 280, 2018.
4. Ding, J. and X. Zhang, "Jet engine modulation signatures of propeller aircraft in air-defense radar signals," Journal of Tsinghua University (Science and Technology), Vol. 3, 418-421, 2003.
5. Wang, B., "Study on classification of airplane targets based on micro-Doppler effect,", Xidian University, 2015.
6. Yang, W., et al., "Automatic feature extraction from insufficient JEM signals based on compressed sensing method," 2015 Asia-Pacific Microwave Conference, Vol. 2, 1-3, 2016.
7. Ebrahimi, S., et al., "Iris recognition system based on fractal dimensions using improved box counting," Journal of Information Science and Engineering, Vol. 35, No. 2, 275-290, 2018.
8. Silva, P. M. and J. B. Florindo, "Fractal measures of image local features: An application to texture recognition," Multimedia Tools and Applications, Vol. 80, 14213-14229, 2021.
9. Ni, J., et al., "Target classification of low-resolution radar based on fractional brown feature," Modern Radar, Vol. 33, No. 6, 46-48, 2011.
10. Li, Q. and W. Xie, "Target classification with low-resolution surveillance radars based on multifractal features," Progress In Electromagnetics Research B, Vol. 45, 291-308, 2012.
11. Li, Q. and W. 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.
12. Zhang, H., Q. Li, C. Rong, and X. Yuan, "Target classification with low-resolution radars based on multifractal features in fractional Fourier domain," Progress In Electromagnetics Research M, Vol. 79, 51-60, 2019.
13. Qu, Z., X. Mao, and C. Hou, "Radar signal recognition based on singular value entropy and fractal dimension," Systems Engineering and Electronics, Vol. 40, No. 2, 303-307, 2018.
14. Chen, C., et al., "A new method for sorting unknown radar emitter signal," Chinese Journal of Electronics, Vol. 23, No. 3, 499-502, 2014.
15. Huo, Y., Y. Fang, and X. Long, "Lightning electric field signals recognition based on EMD and fractal theory," Journal of Northwest Normal University (Natural Science), Vol. 55, No. 5, 33-38+50, 2019.
16. Wang, R., M. Xiang, and C. Li, "Denoising FMCW ladar signals via EEMD with singular spectrum constraint," IEEE Geoscience and Remote Sensing Letters, 1-5, 2019.
17. Li, C., et al., "Fault diagnosis of rolling element bearing of correlation coefficient and arrangement entropy based on EEMD," Modular Machine Tool & Automatic Manufacturing Technique, Vol. 8, 1-4, 2020.
18. He, J. and J. Xu, "The multifractal spectrum of a sea clutter using a random walk model," Acta Oceanologica Sinica, Vol. 36, No. 9, 23-26, 2017.
19. Guan, J., et al., "Multifractal correlation characteristic of real sea clutter and low-observable targets detection," Journal of Electronics & Information Technology, Vol. 32, No. 1, 54-61, 2010.
20. Wu, Z. and N. E. Huang, "Ensemble empirical mode decomposition: A noise-assisted data analysis method," Advances in Adaptive Data Analysis, Vol. 1, No. 1, 1-41, 2009.
21. Zhang, Z., Y. Du, and W. Hu, "Waveform entropy-based target detection in HRRPs," Aeronautical Computing Technique, Vol. 6, 51-54, 2007.
22. Li, Q., H. Zhang, Q. Lu, and L. Wei, "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.
23. Yang, H., Y. Cheng, and G. Li, "A denoising method for ship radiated noise based on Spearman variational mode decomposition, spatial-dependence recurrence sample entropy, improved wavelet threshold denoising, and Savitzky-Golay filter," Alexandria Engineering Journal, Vol. 60, No. 3, 3379-3400, 2021.
24. Zhang, H. and Q. Li, "Target classification based on multifractal features in fractional Fourier transform domain," Radar Science and Technology, Vol. 17, No. 6, 647-654, 2019.
25. Gerdan, D., A. Beyaz, and M. Vatandas, "Classification of apple varieties: Comparison of ensemble learning and naive bayes algorithms in H2O framework," Journal of Agricultural Faculty of Gaziosmanpasa University, Vol. 37, No. 1, 9-16, 2020.
26. Rao, B., et al., "ACPred-Fuse: Fusing multi-view information improves the prediction of anticancer peptides," Briefings Bioinformatics, Vol. 21, No. 5, 1846-1855, 2020.
27. Lobo, J. M., A. Jiménez-Valverde, and R. Real, "AUC: A misleading measure of the performance of predictive distribution models," Global Ecology and Biogeography, Vol. 17, No. 2, 145-151, 2008.
28. Liu, S. and F. Zhang, "Multifractal evaluation and classification of 3-D jointed rock mass quality," Rock and Soil Mechanics, Vol. 7, 1116-1121, 2004.
29. Hu, J., Q. Li, Q. Zhang, and Y. Zhong, "Aircraft target classification method based on EEMD and multifractal," Progress In Electromagnetics Research M, Vol. 99, 223-231, 2021.
30. Zhang, H. and Q. Li, "Target classification with low-resolution radars based on multifractal correlation characteristics in fractional Fourier domain," Progress In Electromagnetics Research C, Vol. 94, 161-176, 2019.