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Communication Dans Un Congrès Année : 2017

An approach for evaluating performance of magnetic-field based indoor positioning system: neural network

Résumé

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. Thaks 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 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
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Dates et versions

hal-01691766 , version 1 (24-01-2018)

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Serpil Ustebay, Zuleyha Yiner, M. Ali Aydin, Ahmet Sertbas, Tülin Atmaca. An approach for evaluating performance of magnetic-field based indoor positioning system: neural network. CN 2017 : 24th international conference on Computer Networks, Jun 2017, Lądek Zdrój, Poland. pp.412 - 421, ⟨10.1007/978-3-319-59767-6_32⟩. ⟨hal-01691766⟩
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