volume | PIER Journals

Abstract

In this paper, a novel approach, namely nonlinear subprofile space (NSS), is proposed for radar target recognition using high-resolution range profile (HRRP). First, the HRRP samples are mapped into a high-dimensional feature space using nonlinear mapping. Second, the nonlinear features, namely nonlinear subprofiles, are extracted by nonlinear discriminant analysis. Then, for each class, the nonlinear subprofile space is formed using all the training nonlinear subprofiles of class. Finally, the minimum hyperplane distance classifier (MHDC) is used for classification. The aim of NSS method is to represent the feature area of target using nonlinear subprofile space, and effectively measure the distance between the test HRRP and feature area via minimum hyperplane distance (MHD) metric. The experimental results of measured data show that the proposed method has better performance of recognition than KPCA and KFDA.

1. Li, H. J. and S. H. Yang, "Using range profiles as feature vectors to identify aerospace objects," IEEE Trans. Antennas and Propagation, Vol. 41, No. 3, 261-268, Mar. 1993.
doi:10.1109/8.233138 Google Scholar

2. Zyweck, A. and R. E. Bogner, "Radar target classification of commercial aircraft," IEEE Trans. Aerospace Electronic Systems, Vol. 32, No. 2, 598-606, Apr. 1996.
doi:10.1109/7.489504 Google Scholar

3. Eom, K. B. and R. Chellappa, "Noncooperative target classification using hierarchical modeling of high-range resolution radar signatures," IEEE Trans. Signal Processing, Vol. 45, No. 9, 2318-2326, Sep. 1997.
doi:10.1109/78.622954 Google Scholar

4. Kim, K. T., D. K. Seo, and H. T. Kim, "Efficient radar target recognition using the MUSIC algorithm and invariant feature," IEEE Trans. Antennas and Propagation, Vol. 50, No. 3, 325-337, Mar. 2002.
doi:10.1109/8.999623 Google Scholar

5. Nelson, D. E., J. A. Starzyk, and D. D. Ensley, "Iterated wavelet transformation and discrimination for HRR radar target recognition," IEEE Trans. System Man and Cybernetics-part: System and Humans, Vol. 33, No. 1, 52-57, Jan. 2003.
doi:10.1109/TSMCA.2003.808253 Google Scholar

6. Wong, S. K., "Non-cooperative target recognition in the frequency domain," IEE Proc. Radar Sonar Navig, Vol. 151, No. 2, 77-84, Apr. 2004.
doi:10.1049/ip-rsn:20040197 Google Scholar

7. Du, L., H. W. Liu, Z. Bao, and J. Y. Zhang, "A two-distribution compounded statistical model for radar HRRP target recognition," IEEE Trans. Signal Processing, Vol. 54, No. 6, 2226-2238, Jun. 2006.
doi:10.1109/TSP.2006.873534 Google Scholar

8. Chen, B., H. W. Liu, and Z. Bao, "PCA and kernel PCA for radar high range resolution profiles recognition," 2005 IEEE International Radar Conference, 9-12, Arlington, VA, USA, May 2005.

9. Mika, S., G. Ratsch, J. Weston, B. Scholkopf, and K. R. Muler, "Fisher discriminant analysis with kernels," IEEE International Workshop on Neural Networks for Signal Processing, Berlin, Aug. 1999.

10. 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

11. Novak, L. M. and G. J. Owirka, "Radar target recognition using an eigen-image approach," IEEE National Radar Conference, Atlanta, USA, Mar. 29--31, 1994.

12. Duda, R., R. Hart, and D. Stock, Pattern Classification, Wiley, 2002.

Dr. Daiying Zhou

Dr. Xiaofeng Shen

Dr. Yangyang Liu