GCN-Enhanced Multidomain Fusion Network for Through-Wall Human Activity Recognition
Xiang Wang, Shisheng Guo, Jiahui Chen, Pengyun Chen, Guolong Cui
Abstract
This letter considers the problem of human activity recognition (HAR) behind the walls using ultra-wideband (UWB) radar. The graph convolutional network (GCN)-enhanced multi-domain fusion network (GMFN) is proposed to improve the recognition performance utilizing the complementarity of the multi-domain features. Specifically, firstly, a multi-branch convolutional neural network (CNN) is proposed to extract the multi-domain features from the range, time-frequency, and range-Doppler domain. Then the multi-domain features are constructed as a graph, and the GCN is employed to fuse the multi-domain features on the graph. Finally, HAR is implemented in the form of the graph classification. The experimental results on the real data show that the proposed GMFN achieves better performance than the state-of-the-art multi-domain fusion HAR methods.