Over the years in the automotive industry, the use of radar sensors in advanced driver assistant systems (ADAS) has been increasing, shining a light on various safety and security concerns to be dealt, that come along with it. The performances of existing sensor technologies such as camera, LIDAR and GPS can be very well complemented by the use of millimeter-wave radar due to its robust performance in various conditions. In assisted or autonomous driving tasks, localization of targets and their height information is important to be determined. Since height information is lacking from automotive radar systems without elevation measurements capabilities, this remains a challenge to be worked on. In the application discussed in this thesis, using the radar specifications providing a high range and velocity resolution, the multi-paths can be resolved in the range-Doppler domain and be used for height estimation techniques. The automotive applications for which short and ultra-short range radar systems are used, require a high range and velocity resolution. In this thesis, a low complexity and fast target height classification algorithm is presented using a FMCW radar.

In the process of having a quick response automotive radar system, low latency DoA estimation technique was employed. To estimate the height properly, knowledge of the ego-velocity is needed. A histogram method can be used to roughly estimate the ego-velocity. The ego-velocity estimate can be refined using an Extended Kalman filter that tracks targets that are supposed to be annotated with height information. Using the vertical doppler beam sharpening technique, the improved ego-motion estimate helps in estimating the elevation angles of the targets and therefore, their corresponding height. An overview of the height estimation process is explained to provide a real time mapping of the target in 3-Dimensions, showcasing the improvement in ego-motion estimation leading to an elevation angle estimation. Simulations and experiments are carried out to validate and verify the algorithm. The results show promising results and also reveal potential improvements and the need for more simulations and experiments to mature the concept further.