Intra-pulse Modulation Recognition of Unknown Radar Emitter Signals Using Support Vector Clustering

  • Gexiang Zhang
  • Haina Rong
  • Weidong Jin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)


Unknown radar emitter signal (RES) recognition is an important issue in modern electronic warfare because the enemy’s RESs are usually uncertain in the battlefield. Although unsupervised classifiers are used generally in many domains, few literatures deal with applications of unsupervised classifiers to RES recognition. In this paper, three unsupervised classifiers including competitive learning neural network (CLNN), self-organizing feature map neural network (SOMNN) and support vector clustering (SVC) are used to recognize unknown RESs. 135 RESs with 7 intra-pulse modulations are used to test the performances of the three classifiers. Experimental results show that SVC is only slightly superior to CLNN and is greatly inferior to SOMNN.


Output Layer Input Layer Feature Selection Algorithm Average Error Rate Winner Neuron 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  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.
    Zhang, G.X., Hu, L.Z., Jin, W.D.: Intra-pulse Feature Analysis of Radar Emitter Signals. Journal of Infrared and Millimeter Waves 23, 477–480 (2004)Google Scholar
  3. 3.
    Turias, I.J., Gonzalez, F.J., Martin, M.L., Galindo, P.L.: A Competitive Neural Network Approach for Meteorological Situation Clustering. Atmospheric Environment 40, 532–541 (2006)CrossRefGoogle Scholar
  4. 4.
    Khanmohammadi, S., Hassanzaseh, I., Zarei Poor, H.R.: Fault Diagnosis Competitive Neural Network with Prioritized Modification Rule of Connection Weights. Artificial Intelligence in Engineering 14, 127–132 (2000)CrossRefGoogle Scholar
  5. 5.
    Meyer-Baese, A., Pilyugin, S.S., Chen, Y.: Global Exponential Stability of Competitive Neural Networks with Different Time Scales. IEEE Transactions on Neural Networks 14, 716–719 (2003)CrossRefGoogle Scholar
  6. 6.
    Simoes, M.G., Furukawa, C.M., Mafra, A.T., Adamowski, J.C.: A Novel Competitive Learning Neural Network Based Acoustic Transmission System for Oil-Well Monitoring. IEEE Transactions on Industry Applications 36, 484–491 (2000)CrossRefGoogle Scholar
  7. 7.
    Kuo, R.J., An, Y.L., Wang, H.S., Chung, W.J.: Integration of Self-Organizing Feature Maps Neural Network and Genetic K-Means Algorithm for Market Segmentation. Expert Systems with Applications 30, 313–324 (2006)CrossRefGoogle Scholar
  8. 8.
    Beckers, M.L.M., Melssen, W.J., Buydens, L.M.C.: A Self-Organizing Feature Map for Clustering Nucleic Acids Application to a Data Matrix Containing A-DNA and B-DNA Dinucleotides. Computer Chemistry 21, 377–390 (1997)CrossRefGoogle Scholar
  9. 9.
    Hsieh, K.H., Tien, F.C.: Self-Organizing Feature Map for Solving Location-Allocation Problems with Rectilinear Distances. Computers and Operation Research 31, 1017–1031 (2004)MATHCrossRefGoogle Scholar
  10. 10.
    Sangole, A., Knopf, G.K.: Visualization of Randomly Ordered Numeric Data Sets Using Spherical Self-Organizing Feature Maps. Computers and Graphics 27, 963–976 (2003)CrossRefGoogle Scholar
  11. 11.
    Ben-Hur, A., Horn, D., Siegelmann, H.T., Vapnik, V.: Support Vector Clustering. Journal of Machine Learning Research 2, 125–137 (2001)CrossRefGoogle Scholar
  12. 12.
    Horn, D.: Clustering via Hilbert space. Physica A 302, 70–79 (2001)MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Zhang, G.X.: Intra-pulse Modulation Recognition of Advanced Radar Emitter Signals Using Intelligent Recognition Method. In: Wang, G.-Y., Peters, J.F., Skowron, A., Yao, Y. (eds.) RSKT 2006. LNCS (LNAI), vol. 4062, pp. 707–712. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Lee, J., Lee, D.: An Improved Cluster Labeling Method for Support Vector Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 461–464 (2005)CrossRefGoogle Scholar
  15. 15.
    Saketha Nath, J., Shevade, S.K.: An Efficient Clustering Scheme Using Support Vector Methods. Pattern Recognition 39, 1473–1480 (2006)MATHCrossRefGoogle Scholar
  16. 16.
    Zhang, G.X., Cao, Z.X., Gu, Y.J.: A Hybrid Classifier Based on Rough Set Theory and Support Vector Machines. In: Wang, L., Jin, Y. (eds.) FSKD 2005. LNCS (LNAI), vol. 3613, pp. 1287–1296. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  17. 17.
    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
  18. 18.
    Zhang, G.X., Hu, L.Z., Jin, W.D.: 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
  19. 19.
    Zhang, G.X., Hu, L.Z., Jin, W.D.: Radar Emitter Signal Recognition Based on Feature Selection Algorithm. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 1108–1114. Springer, Heidelberg (2004)CrossRefGoogle 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 UniversityChengduChina

Personalised recommendations