A Time Domain Artificial Intelligence Radar System Using 33-GHz Direct Sampling for Hand Gesture Recognition Jungwoon Park , Junyoung Jang , Geunhaeng Lee , Hyunmin Koh, Changhwan Kim, and Tae

Abstract

This article introduces a time-domain-based artifi- cial 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.