Intra-pulse modulation recognition of unknown radar emitter signals using support vector clustering

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4223 LNAI, Issue , 2006, Pages 420-429

  Zhang, Gexiang ^a   Rong, Haina ^a   Jin, Weidong ^a  

^a SOUTHWEST JIAOTONG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ELECTRONIC WARFARE; LEARNING SYSTEMS; NEURAL NETWORKS; PULSE MODULATION; SELF ORGANIZING MAPS; SIGNAL PROCESSING;

COMPETITIVE LEARNING NEURAL NETWORK (CLNN); INTRAPULSE MODULATION RECOGNITION; RADAR EMITTER SIGNAL (RES) RECOGNITION; SUPPORT VECTOR CLUSTERING (SVC);

RADAR TARGET RECOGNITION;

EID: 33750571616     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11881599_48     Document Type: Conference Paper

References (19)

1. Zhang, G.X., Hong, 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) 665-670
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 (2004) 477-480
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 (2006) 532-541
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 (2000) 127-132
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 (2003) 716-719
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 (2000) 484-491
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 (2006) 313-324
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 (1997) 377-390
9. Hsieh, K.H., Tien, F.C.: Self-Organizing Feature Map for Solving Location-Allocation Problems with Rectilinear Distances. Computers and Operation Research, 31 (2004) 1017-1031
10. Sangole, A., Knopf, G.K.: Visualization of Randomly Ordered Numeric Data Sets Using Spherical Self-Organizing Feature Maps. Computers and Graphics, 27 (2003) 963-976
11. Ben-Hur, A., Horn, D., Siegelmann, H.T., Vapnik, V.: Support Vector Clustering. Journal of Machine Learning Research, 2 (2001) 125-137
12. Horn, D.: Clustering via Hilbert space. Physica A, 302 (2001) 70-79
13. Zhang, G.X.: Intra-pulse Modulation Recognition of Advanced Radar Emitter Signals Using Intelligent Recognition Method. In: Wang, G., et al. (eds.): Rough Set and Knowledge Technology. Lecture Notes in Artificial Intelligence, Vol. 4062. Springer-Verlag, Berlin Heidelberg New York (2006) 707-712
14. Lee, J., Lee, D.: An Improved Cluster Labeling Method for Support Vector Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (2005) 461-464
15. Saketha Nath, J., Shevade, S.K.: An Efficient Clustering Scheme Using Support Vector Methods. Pattern Recognition, 39 (2006) 1473-1480
16. Zharig, G.X., Cao, Z.X., Gu, Y.J.: A Hybrid Classifier Based on Rough Set Theory and Support Vector Machines. In: Wang, L.P., Jin, Y.C. (eds.): Fuzzy Systems and Knowledge Discovery. Lecture Notes in Artificial Intelligence, Vol. 3613. Springer-Verlag, Berlin Heidelberg New York (2005) 1287-1296
17. Zhang, G.X.: Support Vector Machines with Huffman Tree Architecture for Multi-Class 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) 24-33
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.X. (eds.): Computational and Information Science. Lecture Notes in Computer Science, Vol. 3314. Springer-Verlag, Berlin Heidelberg New York (2004) 1020-1026
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.H. (eds.): Advances in Artificial Intelligence. Lecture Notes in Artificial Intelligence, Vol. 3339. Springer-Verlag, Berlin Heidelberg New York (2004) 1108-1114