{"title":"基于Kolmogorov-Wiener滤波和门控递归神经网络的Wi-Fi传感人体检测","authors":"P. Shibaev, A. Chupakhin","doi":"10.15514/ispras-2022-34(2)-11","DOIUrl":null,"url":null,"abstract":"Using Received Signal Strength Indicator (RSSI) values to detect human presence is a well-known Wi-Fi sensing technique. In this paper, an overview of existing algorithms solving the problem is considered. Two new techniques based on the discrete Kolmogorov-Wiener filter and the gated recurrent unit neural network are proposed. Human detection experiment results are presented along with algorithms' accuracy analysis.","PeriodicalId":33459,"journal":{"name":"Trudy Instituta sistemnogo programmirovaniia RAN","volume":"2 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Wi-Fi sensing Human Detection with Kolmogorov-Wiener Filter and Gated Recurrent Neural Networks\",\"authors\":\"P. Shibaev, A. Chupakhin\",\"doi\":\"10.15514/ispras-2022-34(2)-11\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Using Received Signal Strength Indicator (RSSI) values to detect human presence is a well-known Wi-Fi sensing technique. In this paper, an overview of existing algorithms solving the problem is considered. Two new techniques based on the discrete Kolmogorov-Wiener filter and the gated recurrent unit neural network are proposed. Human detection experiment results are presented along with algorithms' accuracy analysis.\",\"PeriodicalId\":33459,\"journal\":{\"name\":\"Trudy Instituta sistemnogo programmirovaniia RAN\",\"volume\":\"2 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Trudy Instituta sistemnogo programmirovaniia RAN\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15514/ispras-2022-34(2)-11\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trudy Instituta sistemnogo programmirovaniia RAN","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15514/ispras-2022-34(2)-11","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Wi-Fi sensing Human Detection with Kolmogorov-Wiener Filter and Gated Recurrent Neural Networks
Using Received Signal Strength Indicator (RSSI) values to detect human presence is a well-known Wi-Fi sensing technique. In this paper, an overview of existing algorithms solving the problem is considered. Two new techniques based on the discrete Kolmogorov-Wiener filter and the gated recurrent unit neural network are proposed. Human detection experiment results are presented along with algorithms' accuracy analysis.
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