volume | PIER Journals

Abstract

Linear double-layered feature extraction (DFE) technique has recently appeared in radar automatic target recognition (RATR). This paper develops this technique to a nonlinear field via parallelizing a series of kernel Fisher discriminant (KFD) units, and proposes a novel kernel-based DFE algorithm, namely, multi-KFD-based linear discriminant analysis (MKFD-LDA). In the proposed method, a multi-KFD (MKFD) parallel algorithm is constructed for feature extraction, and then the projection features on the MKFD subspace are further processed by LDA. Experimental results on radar HRRP databases indicate that, compared with some classical kernel-based methods, the proposed MKFD-LDA not only performs better and more harmonious recognition, but also keeps higher robustness to kernel parameters, lower training computational cost, and competitive noise immunity.

1. Huang, C.-W. and K.-C. Lee, "Application of ICA technique to PCA based radar target recognition," Progress In Electromagnetics Research, Vol. 105, 157-170, 2010.
doi:10.2528/PIER10042305 Google Scholar

2. Huang, C.-W. and K.-C. Lee, "Frequency-diversity RCS based target recognition with ICA projection," Journal of Electromagnetic Waves and Applications, Vol. 24, No. 17-18, 2547-2559, 2010.
doi:10.1163/156939310793675763 Google Scholar

3. Lee, K.-C., C.-W. Huang, and M.-C. Fang, "Radar target recognition by projected features of frequency-diversity RCS," Progress In Electromagnetics Research, Vol. 81, 121-133, 2008.
doi:10.2528/PIER08010206 Google Scholar

4. Lee, K.-C. and J.-S. Ou, "Radar target recognition by using linear discriminant algorithm on angular-diversity RCS," Journal of Electromagnetic Waves and Applications, Vol. 21, No. 14, 2033-2048, 2007.
doi:10.1163/156939307783152902 Google Scholar

5. Secmen, M., "Radar target classification method with high accuracy and decision speed performance using MUSIC spectrum vectors and PCA projection," Radio Science, Vol. 46, No. 5, 1-9, 2011.
doi:10.1029/2011RS004662 Google Scholar

6. Turhan-Sayan, G., "Real time electromagnetic target classification using a novel feature extraction technique with PCA-based fusion," IEEE Transactions on Antennas and Propagation, Vol. 53, No. 2, 766-776, 2005.
doi:10.1109/TAP.2004.841326 Google Scholar

7. Wang, F.-F. and Y.-R. Zhang, "The support vector machine for dielectric target detection through a wall," Progress In Electromagnetics Research Letters, Vol. 23, 119-128, 2011. Google Scholar

8. Hsu, C.-W. and C.-J. Lin, "A comparison of methods for multiclass support vector machines," IEEE Trans. on Neural Networks, Vol. 13, No. 2, 415-425, 2002.
doi:10.1109/72.991427 Google Scholar

9. Mika, S., G. Rotsch, J. Weston, et al. "Fisher discriminant analysis with kernels," Neural Networks for Signal Processing --- Proceedings of the IEEE Workshop, 41-48, Aug. 23-25, 1999. Google Scholar

10. Baudat, G. and F. Anouar, "Generalized discriminant analysis using a kernel approach," Neural Comput., Vol. 12, No. 10, 2385-2404, 2000.
doi:10.1162/089976600300014980 Google Scholar

11. Lu, J., K. N. Plataniotis, and A. N. Venetsanopoulos, "Face recognition using kernel direct discriminant analysis algorithms," IEEE Trans. on Neural Networks, Vol. 14, No. 1, 117-126, 2003.
doi:10.1109/TNN.2002.806629 Google Scholar

12. Liu, H.-L. and W.-L. Yang, "Radar target recognition based on generalized discriminant analysis of QR decomposition," Journal of Infrared and Millimeter Waves, Vol. 26, No. 3, 205-208, 2007. Google Scholar

13. Fu, J.-S. and W.-L. Yang, "KFD-based multiclass synthetical discriminant analysis for radar HRRP recognition," Journal of Electromagnetic Waves and Applications, Vol. 26, No. 2-3, 169-178, 2012. Google Scholar

14. Fu, J., X. Deng, and W. Yang, "Radar HRRP recognition based on discriminant information analysis," WSEAS Trans. on Inf. Sci. Appl., Vol. 8, No. 4, 185-201, 2011. Google Scholar

15. Cheng, B., H. Liu, J. Chai, et al. "Large margin feature weighting method via linear programming," IEEE Trans. on Knowl. Data Eng., Vol. 21, No. 10, 1475-1488, 2009.
doi:10.1109/TKDE.2008.238 Google Scholar

16. Du, L., P. Wang, H. Liu, et al. "Bayesian spatiotemporal multitask learning for radar HRRP target recognition," IEEE Trans. on Signal Process., Vol. 59, No. 7, 3182-3196, 2011.
doi:10.1109/TSP.2011.2141664 Google Scholar

17. Lim, H. and N. H. Myung, "High resolution range profile-jet engine modulation analysis of aircraft models," Journal of lectromagnetic Waves and Applications, Vol. 25, No. 8-9, 1092-1102, 2011.
doi:10.1163/156939311795762088 Google Scholar

18. Cheng, X. X., H. S. Chen, X. M. Zhang, B. L. Zhang, and B.-I. Wu, "Cloaking a perfectly conducting sphere with rotationally uniaxial nihility media in monostatic radar system," Progress In Electromagnetics Research, Vol. 100, 285-298, 2010.
doi:10.2528/PIER09112002 Google Scholar

19. Chang, Y.-L., C.-Y. Chiang, and K.-S. Chen, "SAR image simulation with application to target recognition," Progress In Electromagnetics Research, Vol. 119, 35-57, 2011.
doi:10.2528/PIER11061507 Google Scholar

20. Gorshkov, S. A., S. P. Leschenko, V. M. Orlenko, et al. Radar Target Backscattering Simulation Software and User's Manual, Artech House, 2002.

21. Yoldemir, A. B., R. Gurcan, G. B. Kaplan, et al. "Comparative analysis of clutter suppression techniques for landmine detection using ground penetrating radar," Proc. SPIE Int. Soc. Opt. Eng., Vol. 80171, 1-8, Apr. 25-29, 2011. Google Scholar

Dr. Jian-Sheng Fu

Dr. Kuo Liao

Dr. Wanlin Yang