摘要
针对现有无线射频信号的手势识别研究中的数据预处理和特征利用问题,该文提出一种用于调频连续波(FMCW)雷达的时空压缩特征表示学习的手势识别算法。首先对手部反射的毫米波雷达回波信号的距离-多普勒(RD)图进行静态干扰去除和动目标点筛选,减少杂波对手势信号的干扰,同时减少计算数据量;然后提出一种压缩手势时空特征的表示方法,利用动目标点的主导速度来表示手势的运动特征,实现多维特征的压缩映射,并保留手势运动的关键特征信息;最后设计了一个单通道的卷积神经网络(CNN)来学习和分类多维手势特征信息并应用于多用户和多位置的手势识别。实验结果表明,与现有其他手势识别算法相比,该文提出的手势识别方法在识别精度、实时性以及泛化能力上都具有明显的优势。
To solve the problems of data preprocessing and feature utilization in the existing work of gesture recognition of radio frequency signals,a gesture recognition algorithm for spatio-temporal compressed feature representation learning of Frequency Modulated Continuous Wave(FMCW)millimeter wave radar is proposed.First,static interference removal and moving target point filtering are performed on the Range-Doppler(RD)image of the FMCW radar echo reflected by the hand,which could reduce the interference of clutter on the gesture signal,and also reduce greatly the calculation of the data.Then,a method for compressing the spatialtemporal features of gesture is adopted to realize the compression mapping of multidimensional features using the dominant velocity of the moving target point to represent the motion characteristics of the gesture,which includes the key feature information of the gesture motion.Finally,a single channel Convolutional Neural Network(CNN)is designed to learn and classify multidimensional gesture feature information in multi-user and multi-location gesture application scenes.Experimental results show that the proposed gesture recognition method has significant performance in recognition accuracy,real-time performance and generalization ability.
作者
韩崇
韩磊
孙力娟
郭剑
HAN Chong;HAN Lei;SUN Lijuan;GUO Jian(College of Computer,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks,Nanjing 210003,China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2022年第4期1274-1283,共10页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61873131,61872194,61902237)。
关键词
毫米波雷达
手势识别
特征压缩
卷积神经网络
Millimeter wave radar
Gesture recognition
Feature compression
Convolutional Neural Network(CNN)
作者简介
通信作者:韩崇,男,1985年生,博士,副教授,硕士生导师,研究方向为计算机网络和无线感知.hc@njupt.edu.cn;韩磊:男,1997年生,硕士生,研究方向为无线感知;孙力娟:女,1963年生,教授,博士生导师,研究方向为演化计算、物联网和无线感知等;郭剑:男,1978年生,博士,副教授,硕士生导师,研究方向为无线传感器网络和无线感知.
