Vol. 30
Latest Volume
All Volumes
PIERC 142 [2024] PIERC 141 [2024] PIERC 140 [2024] PIERC 139 [2024] PIERC 138 [2023] PIERC 137 [2023] PIERC 136 [2023] PIERC 135 [2023] PIERC 134 [2023] PIERC 133 [2023] PIERC 132 [2023] PIERC 131 [2023] PIERC 130 [2023] PIERC 129 [2023] PIERC 128 [2023] PIERC 127 [2022] PIERC 126 [2022] PIERC 125 [2022] PIERC 124 [2022] PIERC 123 [2022] PIERC 122 [2022] PIERC 121 [2022] PIERC 120 [2022] PIERC 119 [2022] PIERC 118 [2022] PIERC 117 [2021] PIERC 116 [2021] PIERC 115 [2021] PIERC 114 [2021] PIERC 113 [2021] PIERC 112 [2021] PIERC 111 [2021] PIERC 110 [2021] PIERC 109 [2021] PIERC 108 [2021] PIERC 107 [2021] PIERC 106 [2020] PIERC 105 [2020] PIERC 104 [2020] PIERC 103 [2020] PIERC 102 [2020] PIERC 101 [2020] PIERC 100 [2020] PIERC 99 [2020] PIERC 98 [2020] PIERC 97 [2019] PIERC 96 [2019] PIERC 95 [2019] PIERC 94 [2019] PIERC 93 [2019] PIERC 92 [2019] PIERC 91 [2019] PIERC 90 [2019] PIERC 89 [2019] PIERC 88 [2018] PIERC 87 [2018] PIERC 86 [2018] PIERC 85 [2018] PIERC 84 [2018] PIERC 83 [2018] PIERC 82 [2018] PIERC 81 [2018] PIERC 80 [2018] PIERC 79 [2017] PIERC 78 [2017] PIERC 77 [2017] PIERC 76 [2017] PIERC 75 [2017] PIERC 74 [2017] PIERC 73 [2017] PIERC 72 [2017] PIERC 71 [2017] PIERC 70 [2016] PIERC 69 [2016] PIERC 68 [2016] PIERC 67 [2016] PIERC 66 [2016] PIERC 65 [2016] PIERC 64 [2016] PIERC 63 [2016] PIERC 62 [2016] PIERC 61 [2016] PIERC 60 [2015] PIERC 59 [2015] PIERC 58 [2015] PIERC 57 [2015] PIERC 56 [2015] PIERC 55 [2014] PIERC 54 [2014] PIERC 53 [2014] PIERC 52 [2014] PIERC 51 [2014] PIERC 50 [2014] PIERC 49 [2014] PIERC 48 [2014] PIERC 47 [2014] PIERC 46 [2014] PIERC 45 [2013] PIERC 44 [2013] PIERC 43 [2013] PIERC 42 [2013] PIERC 41 [2013] PIERC 40 [2013] PIERC 39 [2013] PIERC 38 [2013] PIERC 37 [2013] PIERC 36 [2013] PIERC 35 [2013] PIERC 34 [2013] PIERC 33 [2012] PIERC 32 [2012] PIERC 31 [2012] PIERC 30 [2012] PIERC 29 [2012] PIERC 28 [2012] PIERC 27 [2012] PIERC 26 [2012] PIERC 25 [2012] PIERC 24 [2011] PIERC 23 [2011] PIERC 22 [2011] PIERC 21 [2011] PIERC 20 [2011] PIERC 19 [2011] PIERC 18 [2011] PIERC 17 [2010] PIERC 16 [2010] PIERC 15 [2010] PIERC 14 [2010] PIERC 13 [2010] PIERC 12 [2010] PIERC 11 [2009] PIERC 10 [2009] PIERC 9 [2009] PIERC 8 [2009] PIERC 7 [2009] PIERC 6 [2009] PIERC 5 [2008] PIERC 4 [2008] PIERC 3 [2008] PIERC 2 [2008] PIERC 1 [2008]
2012-05-30
Radar HRRP Target Recognition Using Multi-Kfd-Based Lda Algorithm
By
Progress In Electromagnetics Research C, Vol. 30, 15-26, 2012
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.
Citation
Jian-Sheng Fu, Kuo Liao, and Wanlin Yang, "Radar HRRP Target Recognition Using Multi-Kfd-Based Lda Algorithm," Progress In Electromagnetics Research C, Vol. 30, 15-26, 2012.
doi:10.2528/PIERC11121804
References

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

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

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

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

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

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

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.

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

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.

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

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

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.

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.

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.

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

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

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

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

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

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