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| 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
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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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References
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- Figure 1. The framework of ELM-AE
- Figure 2. The training process of DELM
- Figure 3. Dropout neural network model
- Figure 4. The HRRP sequence for each aircraft target
- Figure 5. Range profiles of each aircraft target
- Figure 6. The effect of different number of nodes in the first and second hidden layers on the classification performance
- Figure 7. The effect of different hidden nodes on training and test time of network
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Figure 8. The effect of different ridge parameters
$C = [{C_1},{C_2},{C_3}]$ - Figure 9. The effect of Dropout parameters on the classification performance