  Transformers combined with convolutional encoders have been recently used for
hand gesture recognition (HGR) using micro-Doppler signatures. We propose a
vision-transformer-based architecture for HGR with multi-antenna
continuous-wave Doppler radar receivers. The proposed architecture consists of
three modules: a convolutional encoderdecoder, an attention module with three
transformer layers, and a multi-layer perceptron. The novel convolutional
decoder helps to feed patches with larger sizes to the attention module for
improved feature extraction. Experimental results obtained with a dataset
corresponding to a two-antenna continuous-wave Doppler radar receiver operating
at 24 GHz (published by Skaria et al.) confirm that the proposed architecture
achieves an accuracy of 98.3% which substantially surpasses the
state-of-the-art on the used dataset.
