Abstract: Concerning that using camera and sensor to detect human motion has the shortcomings of difficult deployment, expensive device and blind zone, a method of human motion detection using Wireless Fidelity (WiFi) signal was proposed. Firstly, the wireless network card was used to receive the Channel State Information (CSI) of the WiFi in the detected environment. Secondly, the Local Outlier Factor (LOF) algorithm and the Hampel filter were used to remove the abnormal CSI data. After the frequency shift caused by the rough synchronization of the network card clock was removed by the linear regression algorithm, the Principal Component Analysis (PCA) was used to reduce dimension and Naive Bayes algorithm was used to classify the CSI data in different cases, which generated a model for judging human movement states. Finally, the model was used to judge the state of human motion. In the experiment, the proposed method can quickly determine the state of human motion and reach the correct rate of 95.62%. The experimental results show that the proposed method can detect and identify the movement of people well.

Key words: Wireless Fidelity (WiFi), Channel State Information (CSI), non-cooperative, motion detection, outlier detection, phase rectification

摘要： 针对用摄像头、传感器等运动检测手段的设备部署复杂、昂贵、有盲区等缺点，提出一种利用无线保真（WiFi）信号进行人体运动检测的方法。首先，使用无线网卡接收被检测环境中WiFi的信道状态信息（CSI）；其次，使用局部离群因子检测（LOF）算法和Hampel滤波器去除异常的CSI数据；然后，用线性回归算法去除因网卡时钟不同步造成的频移误差，再用主成分分析（PCA）降维和朴素贝叶斯算法分类不同情况下的CSI数据，生成用于判断人体运动状况的模型；最终用生成的模型对人体运动状态进行判断。在实验中该方法能快速判断并达到95.62%的正确率。实验结果表明该方法能很好检测识别人的运动。

关键词: 无线保真, 信道状态信息, 非合作式, 运动检测, 异常检测, 相位矫正