Deep radar detector

Daniel Brodeski, Igal Bilik, Raja Giryes

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

27 Scopus citations


While camera and LiDAR processing have been revolutionized since the introduction of deep learning, radar processing still relies on classical tools. In this paper, we introduce a deep learning approach for radar processing, working directly with the radar complex data. To overcome the lack of radar labeled data, we rely in training only on the radar calibration data and introduce new radar augmentation techniques. We evaluate our method on the radar 4D detection task and demonstrate superior performance compared to the classical approaches while keeping real-time performance. Applying deep learning on radar data has several advantages such as eliminating the need for an expensive radar calibration process each time and enabling classification of the detected objects with almost zero-overhead.

Original languageEnglish
Title of host publication2019 IEEE Radar Conference, RadarConf 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728116792
StatePublished - 1 Apr 2019
Externally publishedYes
Event2019 IEEE Radar Conference, RadarConf 2019 - Boston, United States
Duration: 22 Apr 201926 Apr 2019

Publication series

Name2019 IEEE Radar Conference, RadarConf 2019


Conference2019 IEEE Radar Conference, RadarConf 2019
Country/TerritoryUnited States


  • Automotive radar
  • Cognitive radar
  • Deep learning radar
  • Radar target detection


Dive into the research topics of 'Deep radar detector'. Together they form a unique fingerprint.

Cite this