Human Motion Recognition in Small Sample Scenarios Based on GaN
and CNN Models
In the research of radar-based human motion classification and recognition, the traditional manual feature extraction is more complicated, and the echo dataset is generally smaller. In view of this problem, a method of human motion recognition in small sample scenarios based on Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) models is proposed. First, a 77 GHz millimeter wave radar data acquisition system is built to obtain echo data. Secondly, the collected human motion echo data is preprocessed, the micro-Doppler features are extracted, and the range Doppler map (RDM) is used to project the velocity dimension and accumulate the two-dimensional micro-Doppler time-frequency map dataset of the human motion frame by frame. Finally, the deep convolution generative adversarial network (DCGAN) is constructed to achieve data augmentation of the sample set, and the CNN is constructed to realize automatic feature extraction to complete the classification recognition of different human motions. Experimental studies have shown that the combination of GAN and CNN can achieve effective recognition of daily human motions, and the recognition accuracy can reach 96.5%. Compared with the manual feature extraction, the recognition accuracy of CNNs is improved by 7.3%. Compared with the original data set, the system recognition accuracy based on the sample augmentation data set is improved by 2.17%, which shows that the GAN has an excellent performance in human motion recognition in small sample scenarios.