计算机科学
串联(数学)
人工智能
手势
手势识别
雷达
模式识别(心理学)
计算机视觉
融合
卷积神经网络
特征提取
多普勒雷达
基质(化学分析)
语音识别
数学
电信
语言学
哲学
材料科学
组合数学
复合材料
作者
Yang Tian-hong,Hanxu Wu
出处
期刊:International Journal of Microwave and Wireless Technologies
[Cambridge University Press]
日期:2023-08-22
卷期号:: 1-9
标识
DOI:10.1017/s1759078723000995
摘要
Abstract Radar-based hand gesture recognition is a potential noncontact human–machine interaction technique. To enhance the recognition performance of hand gesture, a multidomain fusion-based recognition method using frequency-modulated continuous wave radar is proposed in this article. The received raw echo data of gestures is preprocessed to obtain the range–time matrix, Doppler–time matrix, and range–Doppler–frame tensor. The obtained three-domain radar data corresponding to each gesture are input into the three-channel convolutional neural network for feature extraction. In particular, the extracted features from three-domain data are fused with learnable weight matrices to obtain the final gesture classification results. The experimental results have shown that the classification accuracy of the proposed multidomain fusion network based on learning weight matrix-based fusion is 98.45%, which improves the classification performance compared with the classic average-based fusion and concatenation fusion.
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