| Citation: | Yong WANG, Jinjun WU, Zengshan TIAN, Mu ZHOU, Shasha WANG. Gesture Recognition with Multi-dimensional Parameter Using FMCW Radar[J]. Journal of Electronics & Information Technology, 2019, 41(4): 822-829. doi: 10.11999/JEIT180485
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A multi-parameter convolutional neural network method is proposed for gesture recognition based on Frequency Modulated Continuous Wave (FMCW) radar. A multidimensional parameter dataset is constructed for gestures by performing time-frequency analysis of the radar signal to estimate the distance, Doppler and angle parameters of the gesture target. To realize feature extraction and classification accurately, an end-to-end structured Range-Doppler-Angle of Time (RDA-T) multi-dimensional parameter convolutional neural network scheme is further proposed using multi-branch network structure and high-dimensional feature fusion. The experimental results reveal that using the combined gestures information of distance, Doppler and angle for multi-parameter learning, the proposed scheme resolves the problem of low information quantity of single-dimensional gesture recognition methods, and its accuracy outperforms the single-dimensional methods in terms of gesture recognition by 5%～8%.
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