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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Generative Adversarial Networks for Classification of Micro-Doppler Signatures of Human Activity
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Generative Adversarial Networks for Classification of Micro-Doppler Signatures of Human Activity

机译：用于分类人类活动的生成对抗网络

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摘要

We propose using generative adversarial networks (GANs) for the classification of micro-Doppler signatures measured by the radar. Despite Deep Convolutional Neural Networks (DCNNs) having been used extensively in radar image classification in recent years, their performance could not be fully implemented in the radar field because of the deficiency of the training data set. This is a key issue because of the extremely high labor and monetary costs involved in obtaining radar images. As such, attempts have been made to resolve this issue via the production of radar data by simulation or by the use of transfer learning. In this letter, we propose the use of GANs to produce a large number of micro-Doppler signatures with which to increase the training data set. Once the GANs are trained, a large amount of similar data, with the same distribution as the original data, can be easily generated. The generated fake micro-Doppler images can then be included in the DCNN training process. The proposed method is applied to classifying human activities measured by the Doppler radar. For each human activity, corresponding GANs that generate micro-Doppler signatures for a particular activity are constructed. Using the micro-Doppler signatures produced by the GANs along with the original data, the DCNN is trained. According to the results, the use of GANs improves the accuracy of classification. Moreover, the use of GANs was found to be more effective than the use of transfer learning.
机译：我们建议使用生成的对抗网络（GANS）进行雷达测量的微多普勒签名的分类。尽管在近年来雷达图像分类中已经广泛使用了深度卷积神经网络（DCNN），但由于训练数据集的缺陷，它们的性能无法在雷达场中完全实现。这是一个关键问题，因为获得了雷达图像所涉及的极高的劳动力和货币成本。因此，已经通过模拟或使用转移学习来通过生产雷达数据来解决此问题的尝试。在这封信中，我们提出了使用GAN来生产大量微多普勒签名，从而增加训练数据集。一旦训练了GAN，就可以很容易地生成大量类似的数据，与原始数据相同的分发。然后可以将生成的假微多普勒图像包括在DCNN训练过程中。所提出的方法应用于分类多普勒雷达测量的人类活动。对于每个人类活动，构建了为特定活动产生微多普勒签名的相应GAN。使用由GANS产生的微多普勒签名以及原始数据，培训DCNN。根据结果​​，使用GAN提高了分类的准确性。此外，发现GAN的使用比使用转移学习更有效。

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