In this paper, a region-based deep convolutional neural network
(R-DCNN) is proposed to detect and classify gestures
measured by a frequency-modulated continuous wave radar
system. Micro-Doppler (μD) signatures of gestures are exploited,
and the resulting spectrograms are fed into a neural
network. We are the first to use the R-DCNN for radar-based
gesture recognition, such that multiple gestures could be automatically
detected and classified without manually clipping
the data streams according to each hand movement in advance.
Further, along with the μD signatures, we incorporate
phase-difference information of received signals from an Lshaped
antenna array to enhance the classification accuracy.
Finally, the classification results show that the proposed network
trained with spectrogram and phase-difference information
can guarantee a promising performance for nine gestures.