Wireless indoor localization using Pathloss-based techniques
Serna Marin, Raul (2015)
2015
Master's Degree Programme in Information Technology
Tieto- ja sähkötekniikan tiedekunta - Faculty of Computing and Electrical Engineering
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Hyväksymispäivämäärä
2015-12-09
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201511111701
https://urn.fi/URN:NBN:fi:tty-201511111701
Tiivistelmä
Location based services (LBS) have become very popular in recent years. LBS use location data to provide different features to the user, such as entertainment, security, emergency services, tracking and real-time information. At present time, the Global Positioning System (GPS) is the most popular and reliable solution in the commercial navigation market, but it is used mainly for outdoor areas. Consequently, different lines of investigation have been open, aiming to create alternatives which solve the localization problem for indoor areas. One of these lines of research focuses on using wireless networks as the technology capable to overcome the challenges presented by GPS systems in indoor environments.
In this thesis, we have implemented a real indoor positioning solution based on pathloss models using wireless LAN networks. It performs localization at its finest under the worst conditions. For that purpose, three different WLAN indoor localization techniques have been developed for Android-based mobile devices: AP-Identification (AP-ID), Pathloss-based method using trilateration and Pathloss-based method using Extended Kalman Filters (EKF). The performance of such algorithms is evaluated in terms of Root Mean Square Error (RMSE).
In this thesis, we have implemented a real indoor positioning solution based on pathloss models using wireless LAN networks. It performs localization at its finest under the worst conditions. For that purpose, three different WLAN indoor localization techniques have been developed for Android-based mobile devices: AP-Identification (AP-ID), Pathloss-based method using trilateration and Pathloss-based method using Extended Kalman Filters (EKF). The performance of such algorithms is evaluated in terms of Root Mean Square Error (RMSE).