Utilizing machine learning algorithms for localization using RSSI values of wireless LAN

Abstract

With the development of new technologies, there has been an upsurge in the demand for precise localization in both outdoor and indoor environments. While a Global Positioning System (GPS) provides sufficient positioning precision in outdoor settings, its accuracy declines in indoor scenarios, necessitating the development of novel positioning approaches that function accurately both indoors and outdoors. The use of various Wireless Local Area Network (WLAN) parameters for localization has been conceptualized. In this study, we attempt to do localization using machine learning methods on WLAN Received Signal Strength Indicator (WLAN RSSI) measurements. We compare the performance of multiple machine learning algorithms on the data set to see which can be used to design efficient future localization systems. The proposed study has achieved second place for the problem statement "ITU-ML5G-PS-016: Location estimation using RSSI of wireless LAN" in AI/ML in 5G Challenge 2021 organized by the International Telecommunication Union.