Passive indoor human daily behavior detection method based on channel state information
The daily behavior detection of indoor human based on CSI is developing rapidly in the field of WSN.At present,most of the research is still in the environment of 2.4 GHz,so the detection rate,robustness and overall performance still need to be improved.In order to solve this problem,a passive indoo...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2019-04-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019082/ |
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| author | Xiaochao DANG Yaning HUANG Zhanjun HAO Xiong SI |
| author_facet | Xiaochao DANG Yaning HUANG Zhanjun HAO Xiong SI |
| author_sort | Xiaochao DANG |
| collection | DOAJ |
| description | The daily behavior detection of indoor human based on CSI is developing rapidly in the field of WSN.At present,most of the research is still in the environment of 2.4 GHz,so the detection rate,robustness and overall performance still need to be improved.In order to solve this problem,a passive indoor human behavior detection method HDFi (Human Detection with Wi-Fi) based on CSI signal was proposed.The method was used to detect the indoor human daily behavior in a 5 GHz band environment,which was divided into three steps:data acquisition,data processing,feature extraction,online detection.Firstly,the experiment collected typical daily behavioral data in complex laboratory and relatively empty meeting room.Secondly,the amplitude and phase data with more obvious features were extracted and processed by low-pass filtering to obtain a set of stable and noise-free data,and then the fingerprint database was established effectively.Finally,in the real-time detection stage,the collected data features were classified by SVM algorithm to extract more stable eigenvalues,and a classification model of indoor human daily behavior detection was established,and then matched the data in the fingerprint database.The experimental results show that the proposed method has the characteristics of high efficiency,high precision and good robustness,and the method does not need any testing personnel to carry any electronic equipment,so it has high practicability. |
| format | Article |
| id | doaj-art-8a9dce7bc1e041aaa048d2b19f15357d |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2019-04-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-8a9dce7bc1e041aaa048d2b19f15357d2025-01-14T07:16:47ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2019-04-014016017059726605Passive indoor human daily behavior detection method based on channel state informationXiaochao DANGYaning HUANGZhanjun HAOXiong SIThe daily behavior detection of indoor human based on CSI is developing rapidly in the field of WSN.At present,most of the research is still in the environment of 2.4 GHz,so the detection rate,robustness and overall performance still need to be improved.In order to solve this problem,a passive indoor human behavior detection method HDFi (Human Detection with Wi-Fi) based on CSI signal was proposed.The method was used to detect the indoor human daily behavior in a 5 GHz band environment,which was divided into three steps:data acquisition,data processing,feature extraction,online detection.Firstly,the experiment collected typical daily behavioral data in complex laboratory and relatively empty meeting room.Secondly,the amplitude and phase data with more obvious features were extracted and processed by low-pass filtering to obtain a set of stable and noise-free data,and then the fingerprint database was established effectively.Finally,in the real-time detection stage,the collected data features were classified by SVM algorithm to extract more stable eigenvalues,and a classification model of indoor human daily behavior detection was established,and then matched the data in the fingerprint database.The experimental results show that the proposed method has the characteristics of high efficiency,high precision and good robustness,and the method does not need any testing personnel to carry any electronic equipment,so it has high practicability.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019082/channel state informationbehavior detectionlow pass filteringsupport vector machine |
| spellingShingle | Xiaochao DANG Yaning HUANG Zhanjun HAO Xiong SI Passive indoor human daily behavior detection method based on channel state information Tongxin xuebao channel state information behavior detection low pass filtering support vector machine |
| title | Passive indoor human daily behavior detection method based on channel state information |
| title_full | Passive indoor human daily behavior detection method based on channel state information |
| title_fullStr | Passive indoor human daily behavior detection method based on channel state information |
| title_full_unstemmed | Passive indoor human daily behavior detection method based on channel state information |
| title_short | Passive indoor human daily behavior detection method based on channel state information |
| title_sort | passive indoor human daily behavior detection method based on channel state information |
| topic | channel state information behavior detection low pass filtering support vector machine |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019082/ |
| work_keys_str_mv | AT xiaochaodang passiveindoorhumandailybehaviordetectionmethodbasedonchannelstateinformation AT yaninghuang passiveindoorhumandailybehaviordetectionmethodbasedonchannelstateinformation AT zhanjunhao passiveindoorhumandailybehaviordetectionmethodbasedonchannelstateinformation AT xiongsi passiveindoorhumandailybehaviordetectionmethodbasedonchannelstateinformation |