MA/FP: Examination of radar target lists for smart home applications
Smart home applications are increasingly finding their way into the everyday lives of many people. Audio and camera systems have already established voice and gesture control in some households. However, the audiovisual sensor technology is a relatively strong intrusion into people’s privacy. Radar sensors offer a decisive advantage in this respect. Since the data do not have any immediately recognizable personal reference. For both private and public buildings, radar sensors therefore offer many attractive possibilities for performing tasks, which involve human behavior, even with high data protection requirements. They can be used, for example, to detect people in the building, their general movements and even to measure vital parameters.
In state-of-the-art radar signal processing, several consecutive Fourier transforms transform the radar raw data in the frequency domain, which is better understandable for humans. Subsequently, a constant false alarm rate (CFAR) detection is utilized to determine the actual targets in the scenery and save related parameters of the target in a so-called target list (object, position, velocity, SNR, reflectivity). With the help of this data, machine learning algorithms can be trained to count the number of people in a room, classify the human`s pose or movements.
The task of the research project / thesis would therefore be to build the preprocessing of the radar data for a TI-radar system, extract the target lists and apply machine learning algorithms (e.g. PointNet++) on the data. Moreover, an in-field study is required to be conducted in order to validate the developed algorithms and examine the performance of the system.
Supervisors: Prof. Dr.-Ing. Martin Vossiek, Lukas Engel (M.Sc.)
Date of issue: starting by today
Language: german or english
Previous knowledge: Radar signal processing, python, machine learning, PointNet++