基于多通道调频连续波毫米波雷达的微动手势识别

夏朝阳; 周成龙; 介钧誉; 周涛; 汪相锋; 徐丰

doi:10.11999/JEIT190797

基于多通道调频连续波毫米波雷达的微动手势识别

doi: 10.11999/JEIT190797

复旦大学电磁波信息科学教育部重点实验室上海 200433

基金项目: 国家自然科学基金(61822107)

详细信息

作者简介:
夏朝阳：男，1993年生，博士生，研究方向为雷达信号处理、目标识别和深度学习

周成龙：男，1995年生，硕士生，研究方向为雷达目标识别和深度学习应用

介钧誉：男，1993年生，硕士生，研究方向为雷达目标识别和深度学习应用

周涛：男，1996年生，硕士生，研究方向为雷达目标识别和深度学习应用

汪相锋：男，1995年生，硕士生，研究方向为雷达目标识别和深度学习应用

徐丰：男，1982年生，教授，博士生导师，研究方向为SAR图像解译、电磁散射建模和类脑人工智能

通讯作者:
徐丰　fengxu@fudan.edu.cn

中图分类号: TN958.94
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出版历程
- 收稿日期: 2019-10-16
- 修回日期: 2019-11-27
- 网络出版日期: 2019-12-09
- 刊出日期: 2020-01-21

Micro-motion Gesture Recognition Based on Multi-channel Frequency Modulated Continuous Wave Millimeter Wave Radar

Key Laboratory for Information Science of Electromagnetic Waves(MoE), Fudan University, Shanghai 200433, China

Funds: The National Natural Science Foundation of China (61822107)

摘要

摘要:
该文提出一种基于多通道调频连续波(FMCW)毫米波雷达的微动手势识别方法，并给出一种微动手势特征提取的最优雷达参数设计准则。通过对手部反射的雷达回波进行时频分析处理，估计目标的距离多普勒谱、距离谱、多普勒谱和水平方向角度谱。设计固定帧时间长度拼接的距离-多普勒-时间图特征，与距离-时间特征、多普勒-时间特征、水平方向角度-时间图特征和三者联合特征等，分别对7类微动手势进行表征。根据手势运动过程振幅和速度差异，进行手势特征捕获和对齐。利用仅有5层的轻量化卷积神经网络对微动手势特征进行分类。实验结果表明，相较其他特征，设计的距离-多普勒-时间图特征能够更为准确地表征微动手势，且对未经训练的测试对象具有更好的泛化能力。
- 毫米波雷达 /
- 微动手势识别 /
- 调频连续波 /
- 卷积神经网络
Abstract:
A micro-motion gesture recognition method based on multi-channel Frequency Modulated Continuous Wave (FMCW) millimeter wave radar is proposed, and an optimal radar parameter design criterion for feature extraction of micro-motion gestures is presented. The time-frequency analysis process is performed on the radar echo reflected by the hand, and the range Doppler spectrum, the range spectrum, the Doppler spectrum and the horizontal direction angle spectrum of the target are estimated. Then the range-Doppler-time-map feature is designed, range-time-map feature, Doppler-time-map feature, horizontal-angle-time-map feature, and three-joint feature with fixed frame time length are used to characterize the 7 classes micro-motion gestures, respectively. And these gesture features are captured and aligned according to the difference in amplitude and speed of the gesture motion process. Then a five-layer lightweight convolutional neural network is designed to classify the gesture features. The experimental results show that, the range-Doppler-time-map feature designed in this paper characterizes the micro-motion gesture more accurately and has a better generalization ability for untrained test objects compared with other features.
- Millimeter wave radar /
- Micro-motion gesture recognition /
- Frequency Modulated Continuous Wave(FMCW) /
- Convolutional Neural Network(CNN)

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图 1 实验流程图

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图 2 7类微动手势的动作示意图与5种特征图

下载: 全尺寸图片幻灯片

图 3 单通道与8通道平均的手势帧RD图信噪比对比

下载: 全尺寸图片幻灯片

表 1 不同时间长度特征的分类准确率对比(%)

数据集长度	6帧	8帧	10帧	15帧
平均分类准确率	76.86	91.86	99.14	99.29

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表 2 多种手势表征方法的对比

特征类型	分类方法	5名训练对象平均分类准确率(%)
SC-RDTM	单通道CNN	98.28
CA-RDTM	单通道CNN	99.14
CA-DTM	单通道CNN	97.14
CA-RTM	单通道CNN	88.00
HATM	单通道CNN	71.71
CA-RTM, CA-DTM 与HATM联合	3通道CNN	93.57

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表 3 多种手势表征方法的对比

特征类型	分类方法	测试对象A平均分类准确率(%)	测试对象B平均分类准确率(%)
SC-RDTM	单通道CNN	86.00	84.29
CA-RDTM	单通道CNN	87.71	85.43
CA-DTM	单通道CNN	84.57	83.43
CA-RTM	单通道CNN	27.14	25.42
HATM	单通道CNN	34.28	30.57
CA-RTM, CA-DTM与HATM联合	3通道CNN	65.14	55.71

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表 4 7种微动手势分类的混淆矩阵

		预测类别
		食指双击	食指顺时针绕圈	食指逆时针绕圈	食指拇指分开	食指拇指并拢	拇指在食指上前搓动	拇指在食指上后搓动	准确度(%)
真实类别	食指双击	100	0	0	0	0	0	0	100
	食指顺时针绕圈	0	100	0	0	0	0	0	100
	食指逆时针绕圈	0	0	100	0	0	0	0	100
	食指拇指分开	0	0	0	100	0	0	0	100
	食指拇指并拢	0	0	0	0	98	0	2	98
	拇指在食指上前搓动	0	0	0	0	0	100	0	100
	拇指在食指上后搓动	0	0	0	0	4	0	96	96
	准确度(%)	100	100	100	100	96.08	100	97.96	99.14

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表 5 测试对象A 7类微动手势分类的混淆矩阵

		预测类别
		食指双击	食指顺时针绕圈	食指逆时针绕圈	食指拇指分开	食指拇指并拢	拇指在食指上前搓动	拇指在食指上后搓动	准确度(%)
真实类别	食指双击	46	3	1	0	0	0	0	92
	食指顺时针绕圈	0	35	15	0	0	0	0	70
	食指逆时针绕圈	3	13	34	0	0	0	0	68
	食指拇指分开	0	0	0	49	0	1	0	98
	食指拇指并拢	0	0	0	0	46	0	4	92
	拇指在食指上前搓动	0	0	0	1	0	49	0	98
	拇指在食指上后搓动	0	0	0	0	2	0	48	96
	准确度(%)	93.88	68.63	68	98	95.83	98	92.31	87.71

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表 6 测试对象B 7类微动手势分类的混淆矩阵

		预测类别
		食指双击	食指顺时针绕圈	食指逆时针绕圈	食指拇指分开	食指拇指并拢	拇指在食指上前搓动	拇指在食指上后搓动	准确度(%)
真实类别	食指双击	45	2	3	0	0	0	0	90
	食指顺时针绕圈	0	32	18	0	0	0	0	64
	食指逆时针绕圈	1	16	33	0	0	0	0	66
	食指拇指分开	0	0	0	46	0	4	0	92
	食指拇指并拢	0	0	0	0	48	0	2	96
	拇指在食指上前搓动	0	0	0	2	0	48	0	96
	拇指在食指上后搓动	0	0	0	0	3	0	47	94
	准确度(%)	97.83	64	61.11	95.83	94.12	92.31	95.92	85.43

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