This article introduces a time-domain-based artificial intelligence (AI) radar system for gesture recognition using 33-GS/s direct sampling technique. High-speed sampling using a time-extension method allows AI learning to be applied to a time-domain radar signal reflecting information on both dynamic and static gestures, and thus can recognize not only dynamic but also static gestures. The Vernier clock generators and high-speed active samplers applied with the time-extension technique makes sampling at 33 GS/s possible. A 1-D convolutional neural network and long short-term memory are employed for both static and dynamic gestures and recognition rates of 93.2% and 90.5% are obtained, respectively. The radar system is implemented using a 65-nm CMOS process with a power consumption of 95 mW.

中文翻译：

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使用 33-GHz 直接采样进行手势识别的时域人工智能雷达系统

本文介绍了一种基于时域的人工智能 (AI) 雷达系统，用于使用 33-GS/s 直接采样技术进行手势识别。使用时间扩展方法的高速采样允许将 AI 学习应用于反映动态和静态手势信息的时域雷达信号，从而不仅可以识别动态手势，还可以识别静态手势。游标时钟发生器和高速有源采样器采用时间扩展技术，可以实现 33 GS/s 的采样。静态和动态手势均采用一维卷积神经网络和长短期记忆，识别率分别为 93.2% 和 90.5%。雷达系统采用 65 纳米 CMOS 工艺实现，功耗为 95 毫瓦。