通信学报 ›› 2020, Vol. 41 ›› Issue (8): 43-54.doi: 10.11959/j.issn.1000-436x.2020141
修回日期:
2020-06-08
出版日期:
2020-08-25
发布日期:
2020-09-05
作者简介:
周启臻(1993- ),男,浙江温州人,陆军工程大学博士生,主要研究方向为无线感知和智能建筑|邢建春(1964- ),男,河北正定人,博士,陆军工程大学教授、博士生导师,主要研究方向为国防工程智能化和智能建筑|杨启亮(1975- ),男,河南信阳人,博士,陆军工程大学副教授、硕士生导师,主要研究方向为软件工程、信息物理融合系统和智能建筑|韩德帅(1990- ),男,山东聊城人,博士,火箭军工程大学讲师,主要研究方向为软件工程和国防工程智能化
基金资助:
Qizhen ZHOU1,Jianchun XING1(),Qiliang YANG1,Deshuai HAN2
Revised:
2020-06-08
Online:
2020-08-25
Published:
2020-09-05
Supported by:
摘要:
针对基于深度学习的Wi-Fi人体行为识别技术存在抗噪声能力弱、信号尺寸不兼容和特征提取不充分等问题,提出了一种基于连续图像深度学习的识别方法。首先把时变Wi-Fi信号重构为若干个连续图像帧,确保输入尺寸一致;进而设计低秩分解算法,对噪声湮没的关键运动信息进行分离;同时提出一种时间域和空间域信息融合的深度模型,自动捕捉变长图像序列的时空域特征,并在WiAR数据集和自主采集数据集上对所提方法进行验证。实验结果表明,所提方法平均识别精度分别为0.94和0.96,具备普适场景下的高精度和稳健性。
中图分类号:
周启臻,邢建春,杨启亮,韩德帅. 基于连续图像深度学习的Wi-Fi人体行为识别方法[J]. 通信学报, 2020, 41(8): 43-54.
Qizhen ZHOU,Jianchun XING,Qiliang YANG,Deshuai HAN. Sequential image deep learning-based Wi-Fi human activity recognition method[J]. Journal on Communications, 2020, 41(8): 43-54.
表2
WiAR数据集上识别正确率比较"
方法 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 平均精度 |
文献[ | 0.92 | 0.89 | 0.84 | 0.94 | 0.96 | 0.85 | 0.92 | 0.90 | 0.96 | 0.93 | 0.86 | 0.95 | 0.90 | 0.85 | 0.95 | 0.97 | 0.911 9 |
文献[ | 0.90 | 0.94 | 0.93 | 0.93 | 0.92 | 0.83 | 0.92 | 0.88 | 0.97 | 0.94 | 0.93 | 0.92 | 0.89 | 0.85 | 0.98 | 0.98 | 0.919 4 |
SIL-Fi | 0.92 | 0.98 | 0.93 | 0.96 | 0.94 | 0.85 | 0.94 | 0.86 | 0.99 | 0.97 | 0.99 | 0.94 | 0.90 | 0.99 | 1.00 | 0.93 | 0.942 7 |
表5
自主采集数据集上不同特征提取方法对系统稳健性的影响"
测试人员序号 | 会议室 | 办公室 | ||||||||||
女性1 | 女性2 | 男性1 | 男性2 | 男性3 | 女性1 | 女性2 | 男性1 | 男性2 | 男性3 | |||
手动特征提取 | 完备特征[ | 0.78 | 0.80 | 0.81 | 0.77 | 0.77 | 0.72 | 0.75 | 0.81 | 0.76 | 0.80 | |
特征挑选[ | 0.85 | 0.88 | 0.87 | 0.84 | 0.84 | 0.83 | 0.86 | 0.86 | 0.81 | 0.83 | ||
自动特征提取 | CNN[ | 0.93 | 0.97 | 0.97 | 0.94 | 0.93 | 0.89 | 0.92 | 0.95 | 0.93 | 0.92 | |
LSTM[ | 0.92 | 0.95 | 0.94 | 0.95 | 0.94 | 0.91 | 0.93 | 0.93 | 0.94 | 0.93 | ||
SIL-Fi | 0.95 | 0.97 | 0.99 | 0.95 | 0.94 | 0.94 | 0.96 | 0.97 | 0.95 | 0.93 |
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