WGAN-GP-Based Synthetic Radar Spectrogram Augmentation in Human Activity Recognition

Despite deep convolutional neural networks (DCNNs) having been used extensively in radar-based human activity recognition in recent years, their performance could not be fully implemented because of the lack of radar dataset. However, radar data acquisition is difficult to achieve due to the high cost of its measurement. Generative adversarial networks (GANs) can be utilized to generate a large number of similar micro-Doppler signatures with which to increase the training data set. For the training of DCNNs, the quality and diversity of data set generated by GANs is particularly important. In this paper, we propose using a more stable and effective Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to augment the training data set. The classification results from the experimental data have shown the proposed method can improve the classification accuracy of human activity.