Volume 7 Issue 5
Nov.  2018
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Article Navigation >  Journal of Radars  > 2018  >  7(5): 613-621
Zhao Feixiang, Liu Yongxiang, Huo Kai. A Radar Target Classification Algorithm Based on Dropout Constrained Deep Extreme Learning Machine[J]. Journal of Radars, 2018, 7(5): 613-621. doi: 10.12000/JR18048
Citation: Zhao Feixiang, Liu Yongxiang, Huo Kai. A Radar Target Classification Algorithm Based on Dropout Constrained Deep Extreme Learning Machine[J]. Journal of Radars, 2018, 7(5): 613-621. doi: 10.12000/JR18048
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A Radar Target Classification Algorithm Based on Dropout Constrained Deep Extreme Learning Machine

doi: 10.12000/JR18048

  • College of Electronic Science, National University of Defense Technology, Changsha 410073, China

Funds:  The National Natural Science Foundation of China (61422114, 61501481), The Natural Science Fund for Distinguished Young Scholars of Hunan Province (2015JJ1003)
  • Received Date: 2018-06-22
  • Rev Recd Date: 2018-08-29
  • Publish Date: 2018-10-28
    Abstract
  • Radar target classification is very important in military and civilian fields. Extreme Learning Machines (ELMs) are widely used in classification because of their fast learning speed and good generalization performance. However, because of their shallow architecture, ELMs may not effectively capture the data high level abstractions. Although many researchers have proposed the Deep Extreme Learning Machine (DELM), which can be used to automatically learn high level feature representations, the model easily falls into overfitting when the training sample is limited. To address this issue, Dropout Constrained Deep Extreme Learning Machine (DCDELM) is proposed in this paper. The experimental results on the measured radar data show that the accuracy of the proposed algorithm can reach 93.37%, which is 5.25% higher than that of the stacked autoencoder algorithm, and 8.16% higher than that of the traditional DELM algorithm.

     

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