Deep Interference Mitigation and Denoising of Real-World FMCW Radar Signals

J. Rock, M. Toth, P. Meissner, F. Pernkopf

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


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.

Original languageEnglish
Title of host publication2020 IEEE International Radar Conference, RADAR 2020
Number of pages6
ISBN (Electronic)9781728168128
Publication statusPublished - Apr 2020
Event2020 IEEE International Radar Conference: RADAR 2020 - Virtual, Washington, United States
Duration: 28 Apr 202030 Apr 2020


Conference2020 IEEE International Radar Conference
Country/TerritoryUnited States
CityVirtual, Washington

ASJC Scopus subject areas

  • Signal Processing
  • Instrumentation
  • Computer Networks and Communications


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