An Approach for Evaluating Performance of Magnetic-Field Based Indoor Positioning Systems: Neural Network

被引：1

作者：

Ustebay, Serpil ^{[1
]}

Yiner, Zuleyha ^{[1
]}

Aydin, M. Ali ^{[1
]}

Sertbas, Ahmet ^{[1
]}

Atmaca, Tulin ^{[2
]}

机构：

[1] Istanbul Univ, Dept Comp Engn, Istanbul, Turkey

[2] Univ Paris Saclay, Telecom SudParis, CNRS, Lab Samovar, Evry, France

来源：

COMPUTER NETWORKS (CN 2017) | 2017年 / 718卷

关键词：

Magnetic-field indoor positioning systems; Neural network; Pattern recognition network; Cross entropy function; Performance; Accuracy; Support Vector Machines (SVM);

D O I：

10.1007/978-3-319-59767-6_32

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Indoor Positioning Systems are more and more attractive research area and popular studies. They provide direct access of instant location information of people in large, complex locations such as airports, museums, hospitals, etc. Especially for elders and children, location information can be lifesaving in such complex places. Thanks to the smart technology that can be worn, daily accessories such as wristbands, smart clocks are suitable for this job. In this study, the earth's magnetic field data is used to find location of devices. Having less noise rather than other type of data, magnetic field data provides high success. In this study, with this data, a positioning model is constructed by using Artificial Neural Network (ANN). Support Vector Machines(SVM) was used to compare the results of the model with the ANN. Also the accuracy of this model is calculated and how the number of hidden layer of neural network affects the accuracy is analyzed. Results show that magnetic field indoor positioning system accuracy can reach 95% with ANN.

引用

页码：412 / 421

页数：10