[1]吴春香,张建明.无线感知网络中动作识别的滤波算法[J].计算机技术与发展,2018,28(05):86-90.[doi:10.3969/j.issn.1673－629X.2018.05.020]
　WU Chun-xiang,ZHANG Jian-ming.Filtering Algorithm for Motion Ｒecognition in Wireless Sensor Networks[J].,2018,28(05):86-90.[doi:10.3969/j.issn.1673－629X.2018.05.020]
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无线感知网络中动作识别的滤波算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年05期
页码:
86-90
栏目:
智能、算法、系统工程
出版日期:
2018-05-10

文章信息/Info

Title:
Filtering Algorithm for Motion Ｒecognition in Wireless Sensor Networks
文章编号:
1673-629X(2018)05-0086-04
作者:
吴春香; 张建明
南京邮电大学 通信与信息工程学院，江苏 南京 210000
Author(s):
WU Chun-xiang; ZHANG Jian-ming
School of Communication and Information Engineering，Nanjing University of Posts and Telecommunications，Nanjing 210000，China
关键词:
 ; 信道状态信息; 主成分分析; 低通滤波; 均值滤波
Keywords:
channel state information; principal component analysis; low pass filter; mean filter
分类号:
TN925
DOI:
10.3969/j.issn.1673－629X.2018.05.020
文献标志码:
A
摘要:
近年来，一些学者提出了基于 WiFi 信号的人类活动识别系统。它们共同的特点是都使用了 CSI(信道状态信息)，CSI 是运行在 802．11 协议下的 30 个子载波的信息，是在某个时刻对信道状态信息的频率响应的采样，在一定程度上是多径效应的数学表达。然而即使在静态环境中，WiFi 信号中的 CSI 值也容易产生波动，因为 WiFi 设备易受周围电磁噪声的影响。传统去噪方法如采用低通滤波器或均值滤波器，在去除这些脉冲和突发噪声方面表现不佳。对此，提出一种结合使用低通滤波器和主成分分析(PCA)的方法，在去除 CSI 中噪声的同时还降低了 CSI 数据信息的维度，提高了系统的运算效率。实验结果表明，该方法的提取特征相比传统方法去噪效果更为明显，大大提升了识别系统的准确性和精确度。
Abstract:
Recently,the human activity recognition systems based on WiFi have been proposed．Their common characteristic is to use the CSI (channel state information) which is the information of the 30 subcarriers running under the 802.11 protocol,and as the sampling of the frequency response of the channel state information at some point,is the mathematical expression of multipath effect to a certain extent.However,even in a static environment,CSI values in WiFi signals fluctuate because WiFi devices are susceptible to surrounding electromagnetic noises.General denoising methods,such as low －pass filters or mean filters,do not perform well in removing these impulse and bursty noises.For this,we propose a method combines the low pass filter and principal component analysis simultaneously,removing the noise in the CSI,while reducing the dimension of the CSI data and improving the efficiency of the system.Experiments show that the extracted features of the PCA method are more obvious than that of the traditional methods,which greatly enhance the accuracy and precision of the recognition system.

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更新日期/Last Update: 2018-06-28