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Data Augmentation for Imbalanced HRRP Recognition Using Deep Convolutional Generative Adversarial Network
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DOI：10.1109/ACCESS.2020.3032580.png)
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Abstract
En 中文
In radar high-resolution range profile (HRRP) recognition, the recognition accuracy will decline when the training samples in some classes (majority classes) greatly outnumbers other classes (minority classes). To alleviate the above imbalanced problem, an HRRP data augmentation framework is proposed. A one-dimensional (1-D) deep convolutional generative adversarial network (DCGAN) is developed to generate artificial HRRPs. The fidelity of the generated HRRPs is evaluated subjectively in the raw data domain and quantitatively by the similarity in the feature domain. The experimental results show that the generated data are similar to the true HRRPs and demonstrate that the proposed framework outperforms the state-of-the-art oversampling methods when handling the imbalanced problem.
Keywords:
Generative adversarial networks
Gallium nitride
Generators
Feature extraction
Scattering
Training
Radar
High resolution range profile (HRRP)
imbalanced problem
data augmentation
1-D deep convolutional generative adversarial network (DCGAN)
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