LAUSR

Fully supervised deep learning has achieved great success in many fields, but its performance is often hindered by the abundance and quantity of available labeled training data. In the field of radar-based human motion recognition (HMR), obtaining sufficient training data is really a challenge due to the scarcity of labeled data, which causes deep learning methods to fall into an overfitting state easily. To overcome this limitation, we propose a generative adversarial network (GAN)-based semi-supervised learning model for radar-based human motion classification, which can leverage a large amount of unlabeled data to enhance classification performance. In addition, according to the characteristics of multiclassification tasks, we improve the loss function of GAN, leading the model to utilize unlabeled data more effectively. We did comparative experiments on human motion radar data measured by the Doppler radar, and the experimental results show that the proposed model has significant advantages in classification accuracy compared with the other models.