Abstract
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.
Highlights
C ONVENTIONAL radar systems have been used to detect the range, angle, and velocity of objects [1], [2]
In [3], an artificial intelligence (AI) radar system for gesture recognition is implemented by applying machine learning to a conventional frequency-modulated continuous wave (FMCW) radar
During the recognition phase, the AI radar system learns and recognizes the features of the range-Doppler image (RDI) generated according to the gesture movements
Summary
C ONVENTIONAL radar systems have been used to detect the range, angle, and velocity of objects [1], [2]. During the recognition phase, the AI radar system learns and recognizes the features of the RDI generated according to the gesture movements. In this article, a time-domain AI radar system that recognizes both static and dynamic gestures is proposed [6]. An AI radar system is proposed that can process a high-speed signal with a low-speed analog-to-digital convertor (ADC) through the time-extension method to overcome the problem when using a high-speed ADC [11]. During the dead time, the charges in each holding capacitor are transferred to the stage sequentially according to the slow clock This allows conversion of high-speed sampled signals into low-speed signals without loss of information.
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