| IMPROVED INDOOR POSITIONING USING FINGERPRINT TECHNIQUE AND WEIGHTED K-NEAREST NEIGHBOUR |
| Paper ID : 1200-GEOSPATIAL (R4) |
| Authors: |
|
Shervin Naderi Salim *1, Mohammad Mahdi Alizadeh2, Sheida Chamankar1, Harald Schuh3 1Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran 2German Research Center for Geosciences (GFZ), Potsdam, Germany,,,Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran 33 Institute for Geodesy and Geoinformation Science, Technische Universität Berlin, Berlin, Germany,,,German Research Center for Geosciences (GFZ), Potsdam, Germany |
| Abstract: |
| Global Navigation Satellite Systems are not effective when there is no direct line of sight between the user and the satellites, such as indoor environments and dense urban areas. Today, location-based services are used significantly due to their utility and ease of access. The fingerprint method is one of the common methods of determining the location in indoor environments. In this research, the indoor positioning system based on the fingerprint algorithm with a wireless network has been implemented. The positioning system based on the method of nearest neighbour and weighted K-nearest neighbour with two access points has been implemented in two different scenarios. The output accuracy of each technique has been compared to each other. The main goal of this article is to compare the accuracy of positioning with the fingerprint method using the mentioned algorithms and to find the most suitable mode and algorithm for determining the indoor position in most places. The improved weighted nearest neighbour method will have an almost acceptable result in all scenarios and also in the first scenario with dense and regular reference points the weighted K-nearest neighbour method with RMSE=0.2812(m) has provided the best result. In the second scenario with scattered and irregular reference points the weighted K-nearest neighbour with RMSE=0.6735(m) has given lower accuracy result. |
| Keywords: |
| WLAN Positioning, Fingerprint Algorithm, Location-Based Services, Nearest Neighbour, Weighted K-Nearest Neighbour |
| Status : Paper Accepted (Poster Presentation) |
