An evaluation of fingerprint-based indoor localization techniques

Since GPS, as a commonly used positioning system in outdoor environments, cannot be used in indoor environments, localization methods suitable for indoor environments are still being investigated. The fingerprinting method stands out from other indoor localization methods because it can use existing signal sources and can be implemented by ubiquitous devices such as mobile phones. In this study, several classification algorithms used in the fingerprinting method are applied to two datasets obtained from two different environments (home and workplace). Among these classification algorithms, Random Forest achieved the best results with 87% and 74% accuracy rates for these datasets. These results are close to the results reported in previous studies, and the accuracy of the algorithms varies depending on the environment in which the dataset has been formed.