  Radar sensors are crucial for environment perception of driver assistance
systems as well as autonomous cars. Key performance factors are a fine range
resolution and the possibility to directly measure velocity. With a rising
number of radar sensors and the so far unregulated automotive radar frequency
band, mutual interference is inevitable and must be dealt with. Sensors must be
capable of detecting, or even mitigating the harmful effects of interference,
which include a decreased detection sensitivity. In this paper, we evaluate a
Convolutional Neural Network (CNN)-based approach for interference mitigation
on real-world radar measurements. We combine real measurements with simulated
interference in order to create input-output data suitable for training the
model. We analyze the performance to model complexity relation on simulated and
measurement data, based on an extensive parameter search. Further, a finite
sample size performance comparison shows the effectiveness of the model trained
on either simulated or real data as well as for transfer learning. A
comparative performance analysis with the state of the art emphasizes the
potential of CNN-based models for interference mitigation and denoising of
real-world measurements, also considering resource constraints of the hardware.
