Indoor Positioning and Machine Learning Algorithms
Abstract: This master thesis focuses around improving the efficiency and accuracy of existing indoor positioning systems with the help of Machine Learning (ML). Our work is based on Bluetooth Low Energy (BLE) v5.1. Position estimation is currently being carried out using the Least-Squares (LS) method in the framework. Introducing Machine Learning to position estimation can reduce computation time and increase accuracy of the system because of the additional “learning” in the form of Machine Learning models that is done by the system. An attempt has been made to extract information from the Direction-finding feature that BLE v5.1 presents, and combine it with ML to potentially improve the current position estimation. In order to test the efficacy of these ML algorithms, a wide range of data has been used for these experiments. This included data from different simulated indoor environments and from measurements done physically in a real office environment. We have experimented with three different Machine Learning algorithms for classification and regression: Random Forest, Support Vector Machine and k Nearest Neighbors. Each algorithm has shown impressive results with centimetre-level accuracy, indicating that it can be rewarding to explore ML even more for the purpose of indoor positioning.
AT THIS PAGE YOU CAN DOWNLOAD THE WHOLE ESSAY. (follow the link to the next page)