Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 441–446, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-441-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 441–446, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-441-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Oct 2019

18 Oct 2019

IMPROVED INDOOR POSITIONING TECHNIQUE BASED ON A GEOGRAPHIC WEIGHTED REGRESSION

A. Gholami1, P. Pahlavani2, S. Azimi1, and S. Shakibi3 A. Gholami et al.
  • 1Dept. of GIS, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
  • 2School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
  • 3Dept. of RS and GIS, Faculty of Geography, University of Tehran, Tehran, Iran

Keywords: Indoor Positioning, GWR, RSSI, Fingerprinting, Wi-Fi, Genetic Algorithm

Abstract. 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.