Advertisement

Application of Support Vector Machines with Binary Tree Architecture to Advanced Radar Emitter Signal Recognition

  • Gexiang Zhang
  • Haina Rong
  • Weidong Jin
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 345)

Abstract

Classifier design is an important issue in radar emitter signal (RES) recognition in which respondence time is a very important and strict performance criterion. For computational efficiency, the multiclass support vector machines (SVMs) with binary tree architecture is introduced to recognize advanced RESs. Resemblance coefficient is used to convert multi-class problems into binary-class problems and consequently the structure of multi-class SVM is obtained. The presented classifier has good classification capability and fast decision-making speed. Experimental results show that the introduced classifier is superior to one-against-all, one-against-one, directed acyclic graph, bottom-up binary tree and several classification methods in the recent literature.

Keywords

Support Vector Machine Directed Acyclic Graph Multiclass Support Vector Machine Correct Recognition Rate Minimum Span Tree Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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., et al., (eds.): Rough Sets and Current Trends in Computing. Lecture Notes in Artificial Intelligence, Vol. 3066. Springer-Verlag, Berlin Heidelberg New York (2004) 665–670Google Scholar
  2. 2.
    Shieh, C.S., Lin, C.T.: A Vector Network for Emitter Identification. IEEE Transaction on Antennas and Propagation. 50 (2002) 1120–1127CrossRefGoogle Scholar
  3. 3.
    Zhang, G.X., Hu, L.Z., Jin, W.D.: Intra-pulse Feature Analysis of Radar Emitter Signals. Journal of Infrared and Millimeter Waves, 23 (2004) 477–480Google Scholar
  4. 4.
    Vapnik, V.N.: An Overview of Statistical Learning Theory. IEEE Transactions on Neural Networks, 10 (1999) 988–999CrossRefGoogle Scholar
  5. 5.
    Wang, L.P. (ed.): Support Vector Machines: Theory and Applications. Springer-Verlag, Berlin Heidelberg New York (2005)Google Scholar
  6. 6.
    Lei, H.S., Govindaraju, V.: Half-Against-Half Multi-class Support Vector Machines. In: Oza, N.C., et al., (eds.): Multiple Classifier Systems. Lecture Notes in Computer Science, Vol. 3541. Springer-Verlag, Berlin Heidelberg New York (2005) 156–164Google Scholar
  7. 7.
    Cheong, S.M., Oh, S.H., Lee, S.Y.: Support Vector Machines with Binary Tree Architecture for Multi-class Classification. Neural Information Processing-Letters and Reviews, 2 (2004) 47–51Google Scholar
  8. 8.
    Schwenker, F., Palm, G.: Tree-Structured Support Vector Machines for Multi-class Pattern Recognition. In: Kittler, J., Roli, F., (eds.): Multiple Classifier Systems. Lecture Notes in Computer Science, Vol. 2096. Springer-Verlag, Berlin Heidelberg New York (2001) 409–417Google Scholar
  9. 9.
    Rifkin, R., Klautau, A.: In Defence of One-Vs-All Classification. Journal ofMachine Learning Research, 5 (2004) 101–141Google Scholar
  10. 10.
    Kreßel, U.: Pairwise Classification and Support Vector Machines. In: Scholkopf, B., et al., (eds.): Advances in Kernel Methods-Support Vector Learning, MIT Press (1999) 185–208Google Scholar
  11. 11.
    Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large Margin DAG’s for Multiclass Classification. Advances in Neural Information Processing Systems, 12 (2000) 547–553Google Scholar
  12. 12.
    Lorena, A.C., Carvalho, A.C.P.L.F.: Minimum Spanning Trees in Hierarchical Multiclass Support Vector Machines Generation. In: Ali, M., Esposito, F., (eds.): Innovations in Applied Artificial Intelligence. Lecture Notes in Artificial Intelligence, Vol. 3533. Springer-Verlag, Berlin Heidelberg New York (2005) 422–431Google Scholar
  13. 13.
    Guo, G.D., Li, S.Z.: Content-Based Audio Classification and Retrieval by Support Vector Machines. IEEE Transactions on Neural Networks, 14 (2003) 209–215CrossRefGoogle Scholar
  14. 14.
    Kahsay, L., Schwenker, F., Palm, G.: Comparison of Multiclass SVM Decomposition Schemes for Visual Object Recognition. In: Kropatsch, W., et al., (eds.): Pattern Recognition. Lecture Notes in Computer Science, Vol. 3663. Springer-Verlag, Berlin Heidelberg New York (2005) 334–341CrossRefGoogle Scholar
  15. 15.
    Lorena, A.C., Carvalho, A.C.P.L.F.: Protein Cellular Localization with Multiclass Support Vector Machines and Decision Trees. In: Setubal, J.C., Verjovski-Almeida, S., (eds.): Advances in Bioinformatics and Computational Biology. Lecture Notes in Bioinformatics, Vol. 3594. Springer-Verlag, Berlin Heidelberg New York (2005) 42–53Google Scholar
  16. 16.
    Zhang, G.X.: Support Vector Machines with Huffman Tree Architecture for Multiclass Classification. In: Lazo, M., Sanfeliu, A., (eds.): Progress in Pattern Recognition, Image Analysis and Applications. Lecture Notes in Computer Science, Vol.3773. Springer-Verlag, Berlin Heidelberg New York (2005) 24–33CrossRefGoogle Scholar
  17. 17.
    Zhang, G.X., Hu, L.Z., Jin, W.D.: Resemblance Coefficient and a Quantum Genetic Algorithm for Feature Selection. In: Suzuki, E., Arikawa, S., (eds.): Discovery Science. Lecture Notes in Artificial Intelligence, Vol. 3245. Springer-Verlag, Berlin Heidelberg New York (2004) 155–168Google Scholar
  18. 18.
    Horton, P., Nakai, K.: Better Prediction of Protein Cellular Localization Sites with k-nearest Neighbor Classifiers. In: Proceedings International Conference of Intelligent Systems in Molecular Biology, Vol.5 (1997) 147–152Google Scholar
  19. 19.
    Horton, P., Nakai, K.: A Probabilistic Classification System for Prediction the Cellular Localization Sites of Proteins. In: Proceedings of International Conference of Intelligent Systems in Molecular Biology, vol.4 (1996) 109–115Google Scholar
  20. 20.
    Cairns, P., Huyck, C., Mitchell, I., Wu, W.X.: A comparison of Categorisation Algorithms for Predicting the Cellular Localization Sites of Proteins. In: Proceedings of 12th International Workshop on Database and Expert Systems Applications (2001) 296–300Google Scholar
  21. 21.
    Chang, C.C., Lin, C.J.: LIBSVM: a library. http://www.csie.ntu.edu.tw/~cjlin/libsvm/Google Scholar
  22. 22.
    Zhang, G.X., Rong, H.N., Hu, L.Z., Jin, W.D.: Entropy Feature Extraction Approach of Radar Emitter Signals. In: Proceedings of International Conference on Intelligent Mechatronics and Automation (2004) 621–625Google Scholar
  23. 23.
    Zhang, G.X., Hu, L.Z., Jin, W.D.: Radar Emitter Signal Recognition Based on Entropy Features. Chinese Journal of Radio Science, 20 (2005) 440–445Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gexiang Zhang
    • 1
  • Haina Rong
    • 1
  • Weidong Jin
    • 1
  1. 1.School of Electrical EngineeringSouthwest Jiaotong UniversityChengdu, SichuanChina

Personalised recommendations