摘要
该文提出一种基于调频连续波(FMCW)雷达多维参数的卷积神经网络手势识别方法。通过对雷达信号进行时频分析,估计手势目标的距离、多普勒和角度参数,构建出手势动作的多维参数数据集。同时,为了进行手势特征提取和精确分类,提出多分支网络结构和高维特征融合的方案,设计出具有端到端结构的RDA-T多维参数卷积神经网络。实验结果表明,结合手势动作的距离、多普勒和角度信息进行多维参数学习,所提方法有效解决了单维参数手势识别方法中手势描述信息量低的问题,且手势识别准确率相较于单参数方法提高了5%～8%。
A multi-parameter convolutional neural network method is proposed for gesture recognition based on Frequency Modulated Continuous Wave(FMCW)radar.A multidimensional parameter dataset is constructed for gestures by performing time-frequency analysis of the radar signal to estimate the distance,Doppler and angle parameters of the gesture target.To realize feature extraction and classification accurately,an end-to-end structured Range-Doppler-Angle of Time(RDA-T)multi-dimensional parameter convolutional neural network scheme is further proposed using multi-branch network structure and high-dimensional feature fusion.The experimental results reveal that using the combined gestures information of distance,Doppler and angle for multi-parameter learning,the proposed scheme resolves the problem of low information quantity of singledimensional gesture recognition methods,and its accuracy outperforms the single-dimensional methods in terms of gesture recognition by 5%~8%.
作者
王勇
吴金君
田增山
周牧
王沙沙
WANG Yong;WU Jinjun;TIAN Zengshan;ZHOU Mu;WANG Shasha(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2019年第4期822-829,共8页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61771083
61704015)
长江学者和创新团队发展计划基金(IRT1299)
重庆市科委重点实验室专项经费基金
重庆市基础与前沿研究计划基金(cstc2017jcyjAX0380
cstc2015jcyjBX0065)
重庆市高校优秀成果转化基金(KJZH17117)
重庆市教委科学技术研究项目(KJ1704083)~~
关键词
FMCW雷达
手势识别
深度学习
卷积神经网络
FMCW radar
Gesture recognition
Deeplearning
Convolutional Neural Network(CNN)
作者简介
通信作者:吴金君xnwujj@foxmail.com
