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.
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页码：1438 / 1445
页数：7
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