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.

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.

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With the advent of advanced driver assistant systems (ADAS) in urban scenarios, a fast and reliable classification and motion estimation of wheel-based vehicles such as cars, trucks or motorcycles is crucial. The fact that the wheels' velocities differ from the vehicle's chassis velocity is exploited. For the first time, a fully automated approach based on the Doppler distribution extracts the exact positions of the wheels. The Normalized Doppler Moment is calculated, describing the Doppler signature of each reflection based on the Doppler distributions of wheels. Locations with high values reveal the positions of the wheels. Besides the classification, the vehicle's orientation and therefore the driving direction can be estimated. Furthermore the position of the rear axle is estimated, which is essential for a reliable prediction of rotational movements and yaw rate estimation. Experimental results with a 77 GHz automotive radar sensor demonstrate the feasibility of the approach.