The growing use of Doppler radars in the automotive field and the constantly increasing measurement accuracy open new possibilities for estimating the motion of the ego-vehicle. The following paper presents a robust and selfcontained algorithm to instantly determine the velocity and yaw rate of the ego-vehicle. The algorithm is based on the received reflections (targets) of a single measurement cycle. It analyzes the distribution of their radial velocities over the azimuth angle. The algorithm does not require any preprocessing steps such as clustering or clutter suppression. Storage of history and data association is avoided. As an additional benefit, all targets are instantly labeled as stationary or non-stationary.

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In this paper a method for interference detection and cancellation for automotive radar systems is proposed. With the growing amount of vehicles equipped with radar sensors, interference mitigation techniques are getting more and more important to maintain good interoperability. Based on the time domain signal of a 76 GHz chirp sequence radar the interfering signals of FMCW radar sensors are identified. This is performed by image processing methods applied to the time-frequencyimage. With the maximally stable extremal regions algorithm the interference pattern in the signal is identified. Once the disturbed samples are known they are zeroed. To avoid any ringing effects in the processed radar image the neighborhood of affected samples is smoothed using a raised cosine window. The effectiveness of the proposed method is demonstrated on real world measurements. The method reveals weak scattering centers of the vehicle, which are occluded by interference otherwise.

With new generations of high-resolution imaging radars the orientation of vehicles can be estimated without temporal filtering. This enables time critical systems to respond even faster. Based on a large data set this paper compares three generic algorithms for the orientation estimation of a vehicle. An experimental MIMO imaging radar is used to highlight the requirements of a robust algorithm. The well-known orientated bounding box and the so-called L-fit are adapted for radar measurements and compared to a brute-force approach. A quality function selects the best fitted model and is a key factor to minimize alignment errors. Moreover, the reliability of the estimation is evaluated with respect to the aspect angle, the distance to the target, and the number of sensors. An approach to estimate the reliability of the current orientation estimation is introduced. It is shown that the root mean square error of the orientation estimation is 9.77 • and 38% smaller compared to the common algorithm. In 50% of the evaluated measurements the orientation estimation error is smaller than 3.73 • .

The availability of high-resolution image radars allows estimating the orientation of vehicles from a single measurement without temporal filtering. This gives the opportunity to react even faster to certain critical traffic scenes. This paper presents an approach for estimating the orientation of a vehicle. The orientated bounding box algorithm known from literature is adapted to this end and a quality function is introduced to choose the optimal bounding box. In addition, a brute-force approach for determining the best possible outcome is presented.