Paper Info
Title
An Approach for Evaluating Performance of Magnetic-Field Based Indoor Positioning Systems: Neural Network.
Abstract
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.
Year
DOI
Venue
2017
10.1007/978-3-319-59767-6_32
Communications in Computer and Information Science
Keywords
Field
DocType
Magnetic-field indoor positioning systems,Neural network,Pattern recognition network,Cross entropy function,Performance,Accuracy,Support Vector Machines (SVM)
Magnetic field,Computer science,Support vector machine,Real-time computing,Smart technology,Artificial neural network,Indoor positioning system
Conference
Volume
ISSN
Citations 
718
1865-0929
0
PageRank 
References 
Authors
0.34
5
5
Name
Order
Citations
PageRank
Serpil Ustebay101.01
Zuleyha Yiner200.34
M. A. Aydin3217.47
Ahmet Sertbas413611.79
Tülin Atmaca56910.93