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I am going to share my works in the field of reinforcement learning and deep learning. You can follow my work from here

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Effect of distance metrics on positioning accuracy

RF fingerprinting based positioning systems have come to the forefront among indoor positioning systems as they provide both high precision and can use existing infrastructure like Wi-Fi access points effectively. In this study, an architecture that constructs the RF signal map of the environment in real-time is proposed. Therefore, with the positioning request, both the fingerprint of the target terminal and RF signal map of the environment are obtained in an online manner. At the stage of comparing the RF fingerprint of the target terminal with RF signal map of the environment, effects of different distance metrics (Manhattan, Euclidean, Chebyshev, Canberra, and Sorensen) and location estimation methods on the positioning accuracy were inspected. It was shown through experiments that the Chebyshev is the best distance metric among others. Moreover, it was seen that location estimation methods have no major impact on positioning accuracy.