Intra-pulse Modulation Recognition of Advanced Radar Emitter Signals Using Intelligent Recognition Method

  • Gexiang Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4062)

Abstract

A new method is proposed to solve the difficult problem of advanced radar emitter signal (RES) recognition. Different from traditional five-parameter method, the method is composed of feature extraction, feature selection using rough set theory and combinatorial classifier. Support vector clustering, support vector classification and Mahalanobis distance are integrated to design an efficient combinatorial classifier. 155 radar emitter signals with 8 intra-pulse modulations are used to make simulation experiments. It is proved to be a valid and practical method.

Keywords

Modulation recogntion radar emitter signal rough set theory support vector clustering support vector classification 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zhang, G.X., Rong, H.N., Jin, W.D., Hu, L.Z.: Radar emitter signal recognition based on resemblance coefficient features. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 665–670. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Kawalec, A., Owczarek, R.: Radar emitter recognition using intrapulse data. In: Proc. of 15th Int. Conf. on MRWC, Warsaw, vol. 2, pp. 435–438 (2004)Google Scholar
  3. 3.
    Shieh, C.S., Lin, C.T.: A vector network for emitter identification. IEEE Transaction on Antennas and Propagation 50, 1120–1127 (2002)CrossRefGoogle Scholar
  4. 4.
    Zhang, G.X.: Intelligent recognition method for radar emitter signals, PhD Dissertation, Southwest Jiaotong University, Chengdu (2005)Google Scholar
  5. 5.
    Zhang, G.X., Jin, W.D., Hu, L.Z.: Discretization of continuous attributes in rough set theory and its application. In: Zhang, J., He, J.-H., Fu, Y. (eds.) CIS 2004. LNCS, vol. 3314, pp. 1020–1026. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Zhang, G.X.: Support vector machines with Huffman tree architecture for multi-class classification. In: Sanfeliu, A., Cortés, M.L. (eds.) CIARP 2005. LNCS, vol. 3773, pp. 24–33. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Rifkin, R., Klautau, A.: In defence of one-vs-all classification. Journal of Machine Learning Research 5, 101–141 (2004)MathSciNetGoogle Scholar
  8. 8.
    Kreβel, U.: Pairwise classification and support vector machines. In: Scholkopf, B., et al. (eds.) Advances in Kernel Methods-Support Vector Learning, pp. 185–208. MIT Press, Cambridge (1999)Google Scholar
  9. 9.
    Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large margin DAG’s for multiclass classification. Advances in Neural Information Processing Systems 12, 547–553 (2000)Google Scholar
  10. 10.
    Guo, G.D., Li, S.Z.: Content-based audio classification and retrieval by support vector machines. IEEE Transactions on Neural Networks 14, 209–215 (2003)CrossRefGoogle Scholar
  11. 11.
    Babiloni, F., Bianchi, L., Semeraro, F., et al.: Mahalanobis distance-based classifiers are able to recognize EEG patterns by using few EEG electrodes. In: Proc. of the 23rd Annual Int. Conf. of EMBS, Istanbul, pp. 651–654 (2001)Google Scholar
  12. 12.
    Ben-Hur, A., Horn, D., Siegelmann, H.T., Vapnik, V.: Support Vector Clustering. Journal of Machine Learning Research 2, 125–137 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gexiang Zhang
    • 1
  1. 1.School of Electrical EngineeringSouthwest Jiaotong UniversityChengduChina

Personalised recommendations