彭宇,
邝晓飞,
张贞凯
江苏科技大学电子信息学院 镇江 212003
基金项目:国家自然科学基金(61701416, 61871203)
详细信息
作者简介:靳标：男，1986年生，博士，副教授，研究方向为雷达目标跟踪与识别、MIMO雷达发射波形设计等
彭宇：男，1996年生，硕士生，研究方向为雷达目标识别、深度学习
邝晓飞：男，1996年生，硕士生，研究方向为MIMO雷达资源分配
张贞凯：男，1982年生，博士，副教授，研究方向为雷达通信一体化、认知雷达
通讯作者:靳标 biaojin@just.edu.cn
中图分类号:TN911.73; TP391.4计量
文章访问数:517
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被引次数:0
出版历程
收稿日期:2020-10-19
修回日期:2021-01-30
网络出版日期:2021-02-24
刊出日期:2021-09-16
Dynamic Gesture Recognition Method Based on Millimeter-wave Radar by One-Dimensional Series Neural Network
Biao JIN,,Yu PENG,
Xiaofei KUANG,
Zhenkai ZHANG
College of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Funds:The National Natural Science Foundation of China (61701416, 61871203)
摘要
摘要:现有的基于雷达传感器的手势识别方法，大多先利用雷达回波对手势的距离、多普勒和角度等信息进行参数估计，得到各种数据谱图，然后再利用卷积神经网络对这些谱图进行分类，实现过程较为复杂。该文提出一种基于串联式1维神经网络(1D-ScNN)的毫米波雷达动态手势识别方法。首先基于毫米波雷达获取动态手势的原始回波，然后利用1维卷积和池化操作对手势特征进行提取，并将这些特征信息输入1维Inception v3结构。最后在网络的末端接入长短期记忆(LSTM)网络来聚合1维特征，充分利用动态手势的帧间相关性，提高识别准确率和训练收敛速度。实验结果表明，该方法实现过程简单，收敛速度快，识别准确率可以达到96.0%以上，高于现有基于数据谱图的手势分类方法。
关键词:雷达目标识别/
手势识别/
卷积神经网络/
毫米波雷达
Abstract:For the most of the existing gesture recognition methods based on the radar sensor, the parameters such as the distance, Doppler, and angle are estimated using the radar echo at first. And then the obtained data spectra are inputted into the convolutional neural networks to classify the gestures. The implementation process is complicated. A dynamic gesture recognition method is proposed based on the millimeter-wave radar using the One-Dimensional Series connection Neural Networks (1D-ScNN) in this paper. Firstly, the original echo of dynamic gesture is obtained by the millimeter-wave radar. The gesture features are extracted by the one-dimensional convolution and pooling operations, and then are inputted into the one-dimensional inception v3 structure. In order to aggregate the one-dimensional features, the Long Short-Term Memory (LSTM) modular is connected to the end of the network. The inter-frame correlation of dynamic gestures echo is fully utilized to improve the recognition accuracy and the convergence speed of training. The experimental results show that the proposed method is simple in implementation and has a fast convergence speed. The classification accuracy can reach more than 96.0%, which is higher than the traditional gesture classification methods.
Key words:Radar target recognition/
Gesture recognition/
Convolutional Neural Networks(CNN)/
Millimeter-wave radar
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