A Hybrid Approach for Indoor Positioning

Year 2016, Volume: 4 Issue: Special Issue-1, 162 - 165, 26.12.2016

Sinem Bozkurt Keser , Uğur Yayan

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.

Keywords

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

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