A Hybrid Approach for Indoor Positioning

Authors

  • Sinem Bozkurt Keser
  • uğur yayan

DOI:

https://doi.org/10.18201/ijisae.2016Special%20Issue-146966

Keywords:

Fingerprinting, indoor positioning, access point selection, clustering, classification, feature selection, expectation maximization, decision tree, received signal strength

Abstract

Positioning systems have wide range of applications with the developing technology. Global Positioning System (GPS) is an efficient solution for outdoor applications but it gives poor accuracy in indoor environment. And, various methods are proposed in the literature such as geometric-based, fingerprint-based, etc. In this study, a hybrid approach that uses both clustering and classification is developed for fingerprint-based method. Information gain based feature selection method is used for selection of the most appropriate features from the WiFi fingerprint dataset in the initial step of this approach. Then, Expectation Maximization (EM) algorithm is applied for clustering purpose. Then, decision tree algorithm is used as a classification task for each cluster. Experimental results indicate that applied algorithms lead to a substantial improvement on localization accuracy. Since, cluster specific decision tree models reduce the size of the tree significantly; computational time of position phase is also reduced.

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Published

26.12.2016

How to Cite

Keser, S. B., & yayan, uğur. (2016). A Hybrid Approach for Indoor Positioning. International Journal of Intelligent Systems and Applications in Engineering, 162–165. https://doi.org/10.18201/ijisae.2016Special Issue-146966

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Section

Research Article