Improving Indoor Positioning via Machine Learning

被引：1

作者：

Mussina, Aigerim ^{[1
]}

Aubakirov, Sanzhar ^{[1
]}

机构：

[1] Al Farabi Kazakh Natl Univ, Dept Comp Sci, Alma Ata, Kazakhstan

来源：

PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON DATA SCIENCE, TECHNOLOGY AND APPLICATIONS (DATA) | 2019年

关键词：

Bluetooth Low Energy; Indoor Positioning; RSSI; Machine Learning; Support Vector Machine;

D O I：

10.5220/0007916601900195

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The problem of real time location system is of current interest. Cities are growing up and buildings become more complex and large. In this paper we will describe the indoor positioning issue on the example of user tracking, while using the Bluetooth Low Energy technology and received signal strength indicator(RSSI). We experimented and compared our simple hand-crafted rules with the following machine learning algorithms: Naive Bayes and Support Vector Machine. The goal was to identify actual position of active label among three possible statuses and achieve maximum accuracy. Finally, we achieved accuracy of 0.95.

引用

页码：190 / 195

页数：6

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