基于Wi-Fi信号的身份识别技术研究

doi:10.11959/j.issn.2096-3750.2021.00213

Abstract

Abstract:

With the constant progress of the information technology, the research on identification is developing towards unawareness and automation, and applied widely in many fields, such as human-computer interaction, smart home and intelligent security.As a non-contact wireless sensing technology, Wi-Fi signal based identification is progressively evolving towards the directions of multi-attribute identification and legality verification.Firstly, the principle of wireless signal-based identification was introduced, and reviews pretreatment processes from the viewpoint of signal processing were reviewed.Then, identification implementation was discussed from two different bases of walking and daily activities.Two major development trends of identification which were multi-attribute identification and legality verification were discussed from function evolution’s views.Finally, the multiple research areas were indicated including hyper-level feature extraction, daily activities’ analysis, identification model construction, and multi-technology fusion as future directions of realizing more robust, diversify and universal identification, which would provide the important support for the research and development of Internet of things perception layer.

Key words: identification, wireless sensing, Wi-Fi signal, multi-attribute identification, legality verification, Internet of things

CLC Number:

TP301.6

Zhongcheng WEI, Xinqiu ZHANG, Bin LIAN, Wei WANG, Jijun ZHAO. A survey on Wi-Fi signal based identification technology[J]. Chinese Journal on Internet of Things, 2021, 5(4): 107-119.

Figures/Tables 11

文章	活动	期刊或会议	数据处理	信号分段	特征提取	分类识别	识别性能
WiFi-ID^[26]	行走	DCOSS’16	带通滤波、DWT	短时能量	统计特征	基于稀疏近似的分类	2～6人，93%～77%
Freesense^[32]	行走	Globecom’16	低通滤波、PCA、DWT	MAD	小波近似系数	DTW＆KNN	2～6人，94.5%～88.9%
WFID^[39]	行走	MobiQuitous’16	PCA	方差	子载波振幅频率	SVM	6～9人，93.1%～91.9%
Nipu^[33]	行走	ICIEV＆icIVPR’18	巴特沃斯低通滤波	短时能量	统计特征	随机森林、DT	5人，78～97.5%，84%～95%
NeuralWave^[34]	行走	IECON’18	小波变换去噪、PCA	—	神经网络特征	ConvNet	24人，87.76%±2.14%
Freesense^[41]	行走	J AMB INTEL HUM COMP’18	低通滤波、PCA、DWT	MAD	小波近似系数	DTW＆KNN	2～6人，94.5%～88.9%
CSIID^[51]	行走	ICCSE '19	—	—	神经网络特征	CNN＆LSTM	2～6人，97.4%～94.8%
XModal～ID^[52]	行走	MobiCom’19	STFT	平均速度	频率分布、躯干的平均	神经网络	8人，top1 75%，
	视频		隐藏点消除、STFT		速度、躯干速度梯度的		top2 90%，
					直方图等		top3 97%
WiDIGR^[49]	行走	IoT～J’20	PCA、带通滤波、STFT、Gabor滤波器	功率谱密度	无线图像特征、行走速度、步长等	SVM	3～6 人，78.28%～92.83%
WiFiU^[38]	步态	UbiComp’16	PCA 、STFT	方差	行走速度、步长、步态周期等	SVM	50人，top1 79.28%，
							top2 89.52%，
							top3 93.05%
WiWho^[35]	步态	IPSN’16	远端多径消除、带通滤波	峰谷检测、短时能量	统计特征、速度特征	DTW	2～6人，92%～80%
Wii^[42]	步态	Sensors’17	PCA、低通滤波	CWT 与小波方差	统计特征	SVM	2～8人，98.7%～90.9%
chen^[40]	步态	UbiComp '17	远端多径消除、低通滤波	短时能量、声波数据	统计特征、行走和周期数据的形状等	SVM	2～6人，92%～82%
	声波		谱减法	方差	步态周期、持续时间、周期数据的形状
AutoID^[46]	步态	AAAI’18	DWT	峰谷检测	Shapelets	C³SL＆AGGM	20人，91%
Nkabiti^[43]	步态	WOCC’19	切比雪夫滤波器	CWT 与小波方差	运动时间、统计特征	LSTM	7人，宿舍 95.5%走廊 96.3%
Xu^[50]	站立	IEEE T INF FOREN SEC’17	背景环境减法、TR	—	人体无线电生物特征	时空共振强度	11人，98.78%
WiPIN^[53]	静止	GLOBECOM’19	巴特沃斯滤波、远端多径消除	—	CSI 序列平均值，统计特征	SVM	30人，92%
Liu^[54]	呼吸	MobiCom’18	基于 EMD 的滤波、子载波选择、FWPT	—	呼吸周期、持续时间等	KNN	20人，93%以上
CP-ID^[37]	静止	PERVASIVE MOB COMPUT’19	远端多径消除、CWT、子载波选择、PCA	—	统计特征、小波近似系数	SVM	2～5 人，84%～65%
BioID^[45]	唇语	UIC’18	巴特沃斯低通滤波、PCA、DWT	短时能量、MAD	小波近似系数	KNN、DT	5人，90%
WiID^[48]	手势	ACM’18	PCA、短时傅里叶变换	—	速度时间子序列	支持向量分布估计	5人，7种手势，90%以上
FingerPass^[55]	手势	MobiHoc’19	远端多径消除、巴特沃斯滤波、子载波选择	振幅微分	神经网络	LSTM SVDD	8种手势，88.7%；7人， 92.6%
Wang^[47]	行走呼吸	IEEE T VEH TECHNOL’18	均值滤波器、PCA、EMD	—	IMF的时频域特征	SoftMax	10人，呼吸97.5%，行走90.4%
Shi^[36]	日常活动	MobiHoc’17	巴特沃斯带通滤波、子载波选择	方差的短时能量	统计特征	DNN	5人，91%；8种原位置活动 97.6%；11人，94%；8种路线行走活动，98.3%
*WiID^[56]	日常活动	UbiComp’17	低通滤波器、PCA、DWT	方差的均值个数	小波近似系数、方向、距离特征、时间区间分布等	HMM＆SVM	10人，五种日常活动， 4种方向活动，90%以上
WiAU^[57]	日常活动	SECON’18	巴特沃斯低通滤波、子载波选择	标准差	神经网络	CNN＆ ResNet	12人身份(行走) 98%， 16种活动90%

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信号分段方法	分段长度	复杂程度	灵敏度	适合场景
方差检测^[38,39,40]	相同	简单	中(适合波形变化明显的数据)	视距
短时能量检测^[26,33]	相同	简单	中(适合波形变化明显的数据)	视距
一阶差分检测^[31]	相同	复杂	强	视距/非视距
平均绝对值偏差检测^[32,41]	不同	复杂	强	视距
CWT检测^[42-43]	不同	复杂	强	视距

文章	活动	具体活动	活动识别率	身份识别率
FingerPass^[55]	手势	手指：向左直线滑动、向右直线滑动、向上直线滑动、向下直线滑动、向左弧度滑动、向右弧度滑动、向中心靠拢、向外方法	5，8，88.7%	5，91.4%
WiID^[48]	手势	手臂：推拉、转动、挥手、甩动、开关门、抬手和放下、手臂往前伸，移动到两侧	5，7，90%以上	5，90% 以上
*WiID^[56]	日常活动	活动：推手、挥手、摆臂、跳跃、行走	10，5， 90%以上	10，90% 以上
		方向：推手、缩手、向前走，向后走	10，4，90%
Shi^[36]	日常活动	位置：工作（打字）、开灯、开柜子、拿文件、桌子前吃东西、开微波炉、开冰箱、开门	5，8，97.6%	5，91%
		路线：门口到座位、座位到门口、座位到灯、灯到座位、座位到柜子、柜子到座位、门口到厨房、厨房到门口	11，8，98.3%	11，94%
WiAU^[57]	日常活动	活动：单手挥舞、高臂挥舞、两手投掷、高抛、画X形、抽签、扔纸、向前踢、向侧边踢、弯腰、鼓掌、行走、打电话、喝水、坐下、向下蹲	12，16，90%	12 （行走活动）， 98%
注：其中活动识别率的表示为（识别人数、活动种类、识别率），身份识别率的表示为（识别人数、识别率）。

文章	作用	志愿者	非法人员数量	识别率
Shi^[36]	指导身份识别	11	8	TRR/TAR 89.7%
Wii^[42]	指导身份识别	8	1～5	TRR 90%～80%
WiFiU^[38]	修正身份识别	50	42	FRR 8.05%，FAR 9.54%
WIPIN^[53]	修正身份识别	30	1～28	0.5×TA+0.5×TR 94%～87%
注：TA：系统将合法人员判定为接收的事件；TR：系统将非法人员判定为拒绝的事件；TAR：系统将合法人员判定为接收的概率；FRR：系统将合法人员判定为拒绝的概率。

A survey on Wi-Fi signal based identification technology

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