论文标题
使用深度学习的多天线雷达系统用于美国手语（ASL）识别
Multi Antenna Radar System for American Sign Language (ASL) Recognition Using Deep Learning
论文作者
论文摘要
本文调查了基于RF的自动美国手语（ASL）识别系统。我们考虑使用时间频率（TF）分析和高分辨率接收到光束成形的关节时空预处理雷达的雷达。联合时间和空间处理使用多个天线传感器提供的额外自由度可以帮助识别两个或多个个体之间的ASL对话。这是通过应用波束形成来收集空间图像以试图通过手和手臂运动同时交流的个人来执行的。时空图像通过卷积神经网络（CNN）进行融合和分类，该卷积神经网络能够辨别不同个体执行的迹象，即使波束形式无法完全分离相应的符号。焦点小组包括具有不同专业知识的个人，并使用Texas Instruments（TI）级联雷达进行77 GHz频率的实时测量。
This paper investigates RF-based system for automatic American Sign Language (ASL) recognition. We consider radar for ASL by joint spatio-temporal preprocessing of radar returns using time frequency (TF) analysis and high-resolution receive beamforming. The additional degrees of freedom offered by joint temporal and spatial processing using a multiple antenna sensor can help to recognize ASL conversation between two or more individuals. This is performed by applying beamforming to collect spatial images in an attempt to resolve individuals communicating at the same time through hand and arm movements. The spatio-temporal images are fused and classified by a convolutional neural network (CNN) which is capable of discerning signs performed by different individuals even when the beamformer is unable to separate the respective signs completely. The focus group comprises individuals with varying expertise with sign language, and real time measurements at 77 GHz frequency are performed using Texas Instruments (TI) cascade radar.
