IMPROVED INDOOR POSITIONING TECHNIQUE BASED ON A GEOGRAPHIC WEIGHTED REGRESSION

Gholami, A.; Pahlavani, P.; Azimi, S.; Shakibi, S.

As technology and science develops and the coming of new equipment’s, standards and different waves spread. Each of these standards and technologies have involved in indoor positioning by various scholars. Various methods have been developed based on different systems, all of which are based on specific methods and concepts. The research tries to do indoor positioning using local Wi-Fi fingerprints and signals. To reduce the error to collect local fingerprints, RSS values are recorded in 4 directions and two times. Geographic weighted regression method has been used to train the network. In this research, a genetic algorithm is used to select the appropriate parameters. Ultimately, the accuracy of the model has reached 1.76 cm. The results show that the increase in the number of access points does not affect the accuracy of position determination, but the choice of the effective access point will be effective in reducing the error.

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Gholami, A. / Pahlavani, P. / Azimi, S. / et al: IMPROVED INDOOR POSITIONING TECHNIQUE BASED ON A GEOGRAPHIC WEIGHTED REGRESSION. 2019. Copernicus Publications.

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