论文标题
使用24 GHz多普勒雷达
Vision Transformer with Convolutional Encoder-Decoder for Hand Gesture Recognition using 24 GHz Doppler Radar
论文作者
论文摘要
变压器与卷积编码器结合使用，最近已使用Micro-Doppler签名用于手势识别（HGR）。我们建议使用Multi-Antenna连续波多普勒雷达接收器为HGR提供基于视觉转换器的体系结构。所提出的架构由三个模块组成：一个卷积编码器，带有三个变压器层的注意模块和一个多层感知器。新型的卷积解码器有助于将具有较大尺寸的斑块喂入注意力模块，以改善特征提取。用与在24 GHz的两种连续波多普勒雷达接收器相对应的数据集获得的实验结果（Skaria等人出版）证实，所提出的架构的准确性达到了98.3％，从而实质上超过了使用的数据集中的最终目的。
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.
