| IMPROVED INDOOR POSITIONING TECHNIQUE BASED ON A COMBINATION OF GENETIC ALGORITHM AND GEOGRAPHIC WEIGHTED REGRESSION |
| Paper ID : 1288-SMPR |
| Authors: |
|
Amin Gholami1, sara shakibi2, Parham Pahlavani *3 1MSc. Graduate, Dept. of GIS, School of Surveying and Geomatic Engineering, College of Engineering, University of Tehran, Tehran, Iran 2Sara Shakibi MSc. student, Dept. of RS and GIS Faculty of geography University of Tehran,Tehran,Iran 3School of Surveying and Geospatial Engineering, College of Eng., University of Tehran, Tehran, Iran |
| Abstract: |
| As technology and science develops and the coming of new equipment‘s, standards, and different waves has been spread. Each of these standards and technologies have involved in indoor positioning by various scholars. Various methods have been developed based on the different systems, all of which are based on specific methods and concepts. This research tries for indoor positioning using local Wi-Fi fingerprints and signals. To reduce the error to collect local fingerprints, received signal strength indication values are recorded in 4 directions and two times. Geographic weighted regression method has been used to train the network. Also, a genetic algorithm is used to select the appropriate parameters. Ultimately, the accuracy of the model has reached 1.76 cm. The results showed 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. |
| Keywords: |
| Indoor Positioning, GWR, RSSI, Fingerprinting, Wi-Fi, Genetic Algorithm |
| Status : Conditional Accept (Poster) |
