{"title":"WiFi iLocate: WiFi based indoor localization for smartphone","authors":"Xiang He, Shirin Badiei, D. Aloi, Jia Li","doi":"10.1109/WTS.2014.6835016","DOIUrl":null,"url":null,"abstract":"In recent years, the increasing popularity of smartphones has promoted the development of location-aware applications. However, highly accurate indoor localization by smartphones remains an open problem. In this paper, we present WiFi iLocate - a system that can help track the location and movement of a smartphone user in indoor environments. The system applies Gaussian process regression to train the collected WiFi received signal strength (RSS) dataset, and particle filter for the estimation of the smartphone user's location and movement. Simulations were conducted in MATLAB to test the performance and provide more insights of the proposed approach. The experiments carried with an iOS device in typical library environment illustrate that our system is an accurate, real-time, press-to-go system.","PeriodicalId":199195,"journal":{"name":"2014 Wireless Telecommunications Symposium","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 Wireless Telecommunications Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WTS.2014.6835016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18
Abstract
In recent years, the increasing popularity of smartphones has promoted the development of location-aware applications. However, highly accurate indoor localization by smartphones remains an open problem. In this paper, we present WiFi iLocate - a system that can help track the location and movement of a smartphone user in indoor environments. The system applies Gaussian process regression to train the collected WiFi received signal strength (RSS) dataset, and particle filter for the estimation of the smartphone user's location and movement. Simulations were conducted in MATLAB to test the performance and provide more insights of the proposed approach. The experiments carried with an iOS device in typical library environment illustrate that our system is an accurate, real-time, press-to-go system.
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