Third party funded individual grant
Acronym: Funklokalisierung
Start date : 15.05.2018
End date : 14.05.2020
This project investigated methods and techniques of machine learning in the field of localization. Deep neural networks have been used to model nonlinear signal propagation in radio-based localization to enable position determination even in difficult metallic environments. Furthermore, it was investigated to what extent the temporal consideration of radio signals by means of recurrent neural networks can provide an added value directly in the localization algorithm and to what extent these can be efficiently combined with classical methods (e.g. Kalman filters).
In addition, we obtained first results regarding a fusion with camera data. Radio-based localization systems have advantages over optical localization technologies when it comes to occlusions. On the other hand, radio-based systems have problems with metallic structures/surfaces, since the radio waves are reflected on metallic surfaces and are thus received via several paths at the receiving antennas. A fusion filter has been developed, which compensates the mutual weaknesses of the systems and allows an exact but at the same time robust tracking.