Radar Gesture Recognition Based on Lightweight Convolutional Neural Network

被引：0
|
作者：
Dong, Yaoyao [1 ]
Qu, Wei [2 ]
Wang, Pengda [1 ]
Jiang, Haohao [1 ]
Gao, Tianhao [1 ]
Shu, Yanhe [3 ]
机构：
[1] Space Engn Univ, Grad Sch, Beijing, Peoples R China
[2] Space Engn Univ, Dept Elect & Opt Engn, Beijing, Peoples R China
[3] PLA, Unit 71239, Beijing, Peoples R China
来源：
SEVENTH ASIA PACIFIC CONFERENCE ON OPTICS MANUFACTURE (APCOM 2021) | 2022年 / 12166卷
关键词：
Gesture Recognition; Millimeter Wave Radar; Clutter Suppression; Feature Extraction; Lightweight Convolutional Neural Network;
D O I：
10.1117/12.2607671
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
In order to effectively overcome the limitations of traditional gesture recognition technology, a method of gesture recognition using millimeter wave radar is proposed. First, according to the introduction of the millimeter-wave radar system and the description of the echo model, the millimeter-wave radar is used to collect the measured data; then the average cancellation method is used to suppress the clutter of the measured data, and the joint time-frequency analysis technology is used for effective feature extraction; The extracted features are used as the model input, and a lightweight convolutional neural network model is improved. Its recognition rate is over 96%, and it has good recognition performance.
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收藏
页数：8
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