A missing data imputation-based emission source identification method

被引：0
|
作者：
Liu H.-J. [1 ]
Liu Z. [1 ]
Jiang W.-L. [1 ]
Zhou Y.-Y. [1 ]
机构：
[1] College of Electronic Science and Engineering, National University of Defense Technology
来源：
Yuhang Xuebao/Journal of Astronautics | 2010年 / 31卷 / 05期
关键词：
Emission source identification; Missing data imputation (MDI); Vector neural network;
D O I：
10.3873/j.issn.1000-1328.2010.05.029
中图分类号：
学科分类号：
摘要：
To deal with the problem of emitter identification caused by the fragmentary feature parameters of the template radars, this paper proposes a new missing data imputation (MDI) based emission source identification method, a vector neural network (VNN) is used to substitute the missing feature parameters and make use of substituted training samples for training VNN to obtain the structure parameters of the network. A number of simulations are presented to demonstrate the performance of the MDI algorithm. Simulation results indicate that the MDI algorithm can not only deal with the missing data, but also can identify the scalar input data and interval-value input data correctly in noisy environment.
引用
收藏
页码：1438 / 1445
页数：7
相关论文
共 19 条
  • [11] Sun X., Si S., Liu C., Support vector machine and its application in the classification of missing data, Proceedings of the 25th Chinese Control Conference, pp. 1148-1151, (2006)
  • [12] Granger E., Rubin M.A., Grossberg S., Lavoie P., Classification of incomplete data using the fuzzy ARTMAP neural network, Proceedings of the International Joint Conference on Neural Networks, 6, pp. 35-40, (2000)
  • [13] Williams D., Liao X., Xue Y., Carin L., Et al., On classification with incomplete data, IEEE Transactions on Pattern Analysis and Machine intelligence, 29, 3, pp. 427-436, (2007)
  • [14] Nowicki R., On combining neuro-fuzzy architectures with the rough set theory to solve classification problems with incomplete data, IEEE Transactions on Knowledge and Data Engineering, 20, 9, pp. 1239-1253, (2008)
  • [15] Guan X., He Y., Yi X., A novel rough set emitter signal recognition model, Journal of Astronautics, 28, 3, pp. 685-688, (2007)
  • [16] Pintelon R., Schoukens J., Frequency domain system identification with missing data, IEEE Transactions on Automatic Control, 45, 2, pp. 364-369, (2000)
  • [17] Shieh C., Lin C., A vector neural network for emitter identification, IEEE Transactions on Antennas and Propagation, 50, 8, pp. 1120-1127, (2002)
  • [18] Ishibuchi H., Fujioka R., Tanaka H., Neural networks that learn from fuzzy if-then rules, IEEE Transactions on Fuzzy Systems, 1, 2, pp. 85-97, (1993)
  • [19] Nava P.A., Interval-based neural networks for soft decisions, Proc. IEEE Int. Conf. on Systems, Man, and Cybernetics, (2001)
12