Radar-based hand gesture recognition is a potential noncontact human–machine interaction technique. To enhance the recognition performance of hand gesture, a multidomain fusion-based recognition method using frequency-modulated continuous wave radar is proposed in this article. The received raw echo data of gestures is preprocessed to obtain the range–time matrix, Doppler–time matrix, and range–Doppler–frame tensor. The obtained three-domain radar data corresponding to each gesture are input into the three-channel convolutional neural network for feature extraction. In particular, the extracted features from three-domain data are fused with learnable weight matrices to obtain the final gesture classification results. The experimental results have shown that the classification accuracy of the proposed multidomain fusion network based on learning weight matrix-based fusion is 98.45%, which improves the classification performance compared with the classic average-based fusion and concatenation fusion.
