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, Tae Wook Kim

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


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.

Original languageEnglish
Article number8976307
Pages (from-to)879-888
Number of pages10
JournalIEEE Journal of Solid-State Circuits
Issue number4
Publication statusPublished - 2020 Apr

Bibliographical note

Funding Information:
Manuscript received August 20, 2019; revised November 14, 2019 and January 6, 2020; accepted January 6, 2020. Date of publication January 30, 2020; date of current version March 26, 2020. This article was approved by Guest Editor Brian Ginsburg. This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korean Government (MSIT) under Grant 2017-0-00418 and in part by the IC Design Education Center (IDEC) for the CAD tool. (Corresponding author: Tae Wook Kim.) The authors are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea (e-mail: taewook.kim@yonsei.ac.kr).

Publisher Copyright:
© 1966-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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