Multidomain Fusion Method for Human Head Movement Recognition

In this article, we consider the problem of human head movement recognition (HMR) using linear frequency-modulated continuous-wave (LFMCW) radar. The multidomain fusion network is proposed to improve the HMR performance, which extracts and fuses the multidomain features from the range and time–frequency (TF) domain. Specifically, the 2-D convolutional neural network (2D-CNN) structure is applied to extract range domain features, and the 3-D convolutional neural network (3D-CNN) structure is designed to extract multitype TF representation (TFR) plots features cooperatively. After that, the learnable convolution weights are used for the adaptive fusion of multidomain features. In addition, the attention mechanism is employed to remove redundant information in the fused features. Finally, experimental results on real data verify the effectiveness of the proposed method.