Deep Learning Based Image Enhancement for Automotive Radar Trained With an Advanced Virtual Sensor

• Published in: IEEE access Vol. 10; pp. 40419 - 40431
• Main Authors: SCHUsLER, Christian, Hoffmann, Marcel, Ullmann, Ingrid, Ebelt, Randolf, Vossiek, Martin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Automotive engineering; Automotive radar; Beamforming; Clutter; Deep learning; deep neural networks; Image enhancement; Machine learning; MIMO communication; Radar; Radar antennas; Radar clutter; Radar imaging; Radar measurement; radar simulation; Ray tracing; Sensors; Sidelobe reduction; Sidelobes; Signal processing; Simulation; Soft sensors; Training; Virtual sensors
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3166227

This paper introduces a novel deep learning based concept for image enhancement and distortion suppression in automotive radar signal processing. The deep neural network (DNN) is trained solely on virtual data that is generated by an automotive MIMO radar ray tracing simulator. The simulator mimics the raw data that would be provided by a specific automotive MIMO radar for which the signal processing is envisaged. This virtual radar sensor, which has the same properties as the real radar, creates the DNN training input data. The unique feature of our approach is that an advanced virtual radar sensor is used to create the target values for the DNN training. The advanced virtual sensor is a simulated radar that works on the same basic principle as the real radar and its resembling virtual sensor, but is significantly upgraded in terms of performance. In our experiments the advanced virtual sensor, e.g., has a notably larger array, i.e. a much better lateral resolution than the real radar. In addition, our advanced virtual sensor is not affected by multi-path effects, clutter, noise, beamforming sidelobes and other typical automotive radar distortions. The paper shows that a DNN trained on this advanced virtual sensor training data can deliver outstanding automotive radar signal processing results not only on simulated data but also on field data. The presented real-world automotive radar measurements show that multi-path, clutter and noise are efficiently suppressed by this DNN and that it has a remarkable ability to sharpen images and suppress sidelobes. The results presented in the paper suggest that the novel advanced DNN training concept based on virtual sensors offers opportunities that also go far beyond the radar signal processing capabilities illustrated. Hence, the concept put forward here is an attractive option for placing DNN-based radar signal processing on a novel and easy-to-implement foundation.