Abstract
As an important part of electronic intelligence (ELINT) and electronic support measurement (ESM) systems, radar signal sorting directly affects the performance of electronic reconnaissance equipment and is a key technology for decision making. This paper discusses several clustering methods which could be used for radar signal sorting. The discussion includes artificial neural networks, classical clustering algorithm and its improvement, and support vector clustering.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aggarwal, C., Han, J., Wang. J.: A framework for projected clustering of high dimensional data streams. In: Proceedings of the 30th VLDB Conference, Toronto, Canada (2004)
Ariadna, M., Alberto, S., Benjamin, F.: Classification of radar jammer FM signals using a neural network. Proc. SPIE 10188, 11–16 (2017)
David Wang, C., Thompson, James: An adaptive data sorter based on probabilistic neural networks. IEEE Naecon Dayton Ohio. 3, 1096–1102 (1991)
Xu, X., Zhou, Y.Y., Lu, Q.Z.: Research on real-time deinter leaving technology for radar intercept system. Syst. Eng. Electron. 23(3), 12–15 (2001)
Lin, Z.Y., Liu, G., Dai, G.X.: Application of Kohonen neural network in radar multi-target sorting. J. Military Eng. Uni. 4(5), 56–59 (2003)
Guo, J., Chen, J.W.: A clustering method for processing unknown radar signals. Syst. Eng. Electron. 28(6), 853–856 (2006)
Han, J., He, M.H., Zhu, Y.Q., et al.: A new method for signal sorting of radar emitter based on multi-parameters. Data Acquisition Process. 24(1), 91–94 (2009)
Wang, X.D., Song, M.Z.: Radar pulse sorting method based on Eidos BSB artificial neural network. Modern Electron. Technol. 23, 6–9 (2010)
Xin, F., Hu, X.X., Liu, Y.: Radar signal sorting algorithm of k-means clustering based on data field. In: 3rd IEEE International Conference on Computer and Communications (ICCC), pp. 2262–2266 (2017)
Marques, J.P., Wu, Y.F.: Pattern Recognition Concepts, Methods and Applications. 2nd edn., pp. 51–74. Tsinghua University Press, Beijing (2002)
Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining Knowl. Discovery II 2, 283–304 (1998)
Huang, Z., Ma, N.: Fuzzy k-modes algorithm for clustering categorical data. IEEE Trans. Fuzzy Syst. 7(4), 446–452 (1999)
Chaturvedi, A.D., Green, P.E., Carroll, J.D.: K-modes clustering. J. Classif. 18(1), 35–56 (2001)
Ding, C., He, X.: K-nearest-neighbor in data clustering: Incorporating local information into global optimization, pp. 584–589. ACM Press, Nicosia (2004)
Fred, A., Leitão, J.: Partitional versus hierarchical clustering using a minimum grammar complexity approach. In: Proceedings of the SSPR&SPR 2000, LNCS vol. 1876, pp. 193–202 (2000)
Gelbard, R., Goldman, O., Spiegler, I.: Investigating diversity of clustering methods: an empirical comparison. Data Knowl. Eng. 63(1), 155–166 (2007)
Kumar, P., Krishna, P.R., Bapi, R.S., De, S.K.: Rough clustering of sequential data. Data Knowl. Eng. 3(2), 183–199 (2007)
Cristianini, N., Shawe-Taylor, J.: An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press (2000)
Zhang, L., Zhou, W.D., Jiao, L.C.: Nuclear clustering algorithm. Chin. J. Comput. 25(6), 587–590 (2002)
Ben-Hur, A., Horn, D., Siegelmann, H.T.: Support vector clustering. Mach. Learn. Res. 2, 125–137 (2000)
Chiang, J.H., Hao, P.Y.: A new kernel-based fuzzy clustering approach: support vector clustering with cell growing. IEEE Trans. Fuzzy Syst. 11, 518–527 (2003)
Girolami, M.: Mercer kernel-based clustering in the feature space. IEEE Trans. Neural Netw. 13(3), 780–784 (2002)
Zhang, D.Q., Chen, S.C.: Fuzzy clustering using kernel method. In: Proceedings of the 2002 International Conference on Control and Automation, pp. 123–127. Xiamen, China (2002)
Cristianini, N., Taylor, J.S.: Kandola, J.S.: Spectral kernel methods for clustering. In NIPS, pp. 649–655 (2001)
Chung, F.R.K.: Spectral graph theory (CBMS Regional Conference Series in Mathematics, No. 92). American Mathematical Society (1997, February)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) (2000)
Kannan, R., Vempala, S., Vetta, A.: On clusterings: good, bad, and spectral. In Proceedings of the 41st Annual Symposium on the Foundation of Computer Science, pp. 367–380. IEEE Computer Society (2000, November)
Dhillon, I.S., Guan, Y., Kulis, B.: A unified view of kernel k-means, spectral clustering and graph partitioning. Technical Report Technical Report TR-04–25. UTCS (2005)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2(2), 121–167 (1998)
Tax, D.M.J., Duin, R.P.W.: Support vector domain description. Pattern Recogn. Lett. 20(11–13), 1191–1199 (1999)
Scholkopf, B., Williamson, R., Smola, A.: Support vector method for novelty detection. Adv. Neural Info. Process. Syst. 12, 582–588 (2000)
Lu, C.K., Jiang, C.Y., Wang, N.S.: A fast algorithm for support vector clustering. J. South China University Technol. 33(1), 6–9 (2005)
Guo, Q., Li, W., Li, H.P.: Application of support vector clustering method in radar signal sorting. Annual Meeting, pp. 237–241 (2005)
Guo, Q., Wang, C.H., Li, W.: Radar signal sorting method based on support vector cluster combined type entropy recognition. J. Xi’an Jiaotong University 44(8), 63–67 (2010)
Xiang, W., Tang, J.L.: Radar signal sorting based on improved support vector clustering. Space Sci. Technol. 27(1), 50–53 (2011)
Li, Z.X., Lu, J.S., Zhang, G.Y.: A radar source SVC sorting method with automatic parameter selection. Electron. Info. Technol. 26(2), 15–20 (2011)
Acknowledgements
This paper was supported in part by the National Natural Science Foundation of China under the Grant No. 61601499, 61701527, and 61601503.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, Sq., Gao, C., Zhang, Q., Zeng, Hy., Bai, J. (2020). The Latest Research on Clustering Algorithms Used for Radar Signal Sorting. In: Jain, V., Patnaik, S., Popențiu Vlădicescu, F., Sethi, I. (eds) Recent Trends in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-9406-5_95
Download citation
DOI: https://doi.org/10.1007/978-981-13-9406-5_95
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9405-8
Online ISBN: 978-981-13-9406-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)