WIFI BASED INDOOR POSITIONING - A MACHINE LEARNING APPROACH

Navigation has become much easier these days mainly due to advancement in satellite technology. The current navigation systems provide better positioning accuracy but are limited to outdoors. When it comes to the indoor spaces such as airports, shopping malls, hospitals or office buildings, to name a few, it will be challenging to get good positioning accuracy with satellite signals due to thick walls and roofs as obstacles. This gap led to a whole new area of research in the field of indoor positioning. Many researches have been conducting experiments on different technologies and successful outcomes have beenseen. Each technology providing indoor positioning capability has its own limitations. In this thesis, different radio frequency (RF) and non-radio frequency (Non-RF) technologies are discussed but focus is set on Wi-Fi for indoor positioning. A demo indoor positioning app is developed for the Technobothnia building at the University of Vaasa premises. This building is already equipped with Wi-Fi infrastructure. A floor plan of the building, radio maps and a fingerprinting database with Wi-Fi signal strength measurements is created with help of tools from HERE technology. The app provides real-time positioning and routing as a future visitor tool. With the exceeding amounts of available data, one of the highly popular fields is applying Machine Learning (ML) to data. It can be applied in many disciplines from medicine to space. In ML, algorithms learn from the data and make predictions. Due to the significant growth in various sensor technologies and computational power, large amounts of data can be stored and processed. Here, the ML approach is also taken to the indoor positioning challenge. An open-source Wi-Fi fingerprinting dataset is obtained from Tampere University and ML algorithms are applied on it for performing indoor positioning. Algorithms are trained with received signal strength (RSS) values with their respective reference coordinates and the user location can be predicted. The thesis provides a performance analysis of different algorithms suitable for future mobile implementations