Abstract
Radar high resolution range profile (HRRP) contains important structural features such as target size and scattering center distribution, which has attracted extensive attention in the field of radar target recognition. Aiming at the problems of class imbalance and insufficient data quality generated by vanilla GAN, an HRRP target recognition method based on improved conditional Wasserstein Variational Autoencoder Generative Adversarial Networks (CWVAEGAN) and one-dimensional convolutional neural network (CWVAEGAN-1DCNN) is proposed. CWVAEGAN class-balancing method introduces the concept of variational lower bound in VAE into GAN, and uses the encoded hidden vector instead of Gaussian noise as generator’s input, which can significantly improve the fidelity of the generated samples and balance the class distribution of the dataset. The balanced dataset is used for classification through a standard 1D-CNN. The experimental results show that the samples generated by CWVAEGAN class-balancing method greatly improve the detection accuracy of 1D-CNN for minority classes, the fidelity of the samples generated by CWVAEGAN is higher than that of other class-balancing methods, and CWVAEGAN-1DCNN has strong HRRP target recognition ability.
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Acknowledgements
This work is supported by the National Science Foundation of China (61806219, 61703426 and 61876189), by National Science Foundation of Shaanxi Provence (2021JM-226) by Young Talent fund of University and Association for Science and Technology in Shaanxi, China (20190108, 20220106), and by and the Innovation Capability Support Plan of Shaanxi, China (2020KJXX-065).
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He, J., Wang, X., Xiang, Q. (2022). A Radar HRRP Target Recognition Method Based on Conditional Wasserstein VAEGAN and 1-D CNN. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13534. Springer, Cham. https://doi.org/10.1007/978-3-031-18907-4_59
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DOI: https://doi.org/10.1007/978-3-031-18907-4_59
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