Passive indoor human localization based on channel state information

被引：0
|
作者：
Wu Z. [1 ]
Xu Q. [1 ]
Wang Z. [2 ]
Chen B. [3 ]
Xuan Q. [1 ]
机构：
[1] College of Information Engineering, Zhejiang University of Technology, Hangzhou
[2] China Comservice Hangzhou Construction Co., Ltd., Hangzhou
[3] College of Art, Zhejiang University of Technology, Hangzhou
来源：
Harbin Gongcheng Daxue Xuebao | / 8卷 / 1328-1334期
关键词：
Channel state information; Degree of confidence; Fingerprint; Multiple input multiple output; Naive Bayes classification; Orthogonal frequency division multiplexing; Outlier; Passive localization;
D O I：
10.11990/jheu.201605001
中图分类号：
学科分类号：
摘要：
To deal with the shortcomings of indoor localization in terms of accuracy and stability, a method based on channel state information (CSI) was proposed to realize passive indoor positioning. A platform was set up with off-the-shelf equipment to collect CSI data. During the offline stage of the method, gathered data from each location were stored as a fingerprint in the database. During the online stage, naive Bayes classification from machine learning was utilized to classify the locations. Furthermore, the degree of confidence was proposed to combine the estimation from different antenna pairs. Result shows that the proposed method can effectively realize passive indoor human localization with an accuracy of more than 90%. © 2017, Editorial Department of Journal of HEU. All right reserved.
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页码：1328 / 1334
页数：6
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