Hybrid Approach for Reflective Surfaces Reconstruction Using Automotive Radar

Aviran Gal, Igal Bilik

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


Automotive radar is the main sensor enabling autonomous driving and advanced driver-assistance system (ADAS) capabilities. Its major role is to provide high-accuracy information on the vehicle surrounding. However, in dense urban environments, multiple objects limit radars' line-of-sight and degrade their performance due to the multipath phenomenon, which generates ghost targets. Efficient radar's operation in the presence of occluding and reflecting surfaces requires their accurate reconstruction. This work proposes a hybrid approach combining the model with the deep neural network to reconstruct the reflected surfaces from the received radar echoes. Accurate reconstruction of reflecting and occluding surfaces is expected to enable automotive radar non-line-of-sight operation and multipath-induced ghost targets mitigation. The ability of the proposed approach to reconstruct the reflective surface is evaluated via simulations, and its superiority over the conventional estimation approaches is demonstrated.

Original languageEnglish
Title of host publication2023 IEEE International Radar Conference, RADAR 2023
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781665482783
StatePublished - 1 Jan 2023
Event2023 IEEE International Radar Conference, RADAR 2023 - Sydney, Australia
Duration: 6 Nov 202310 Nov 2023

Publication series

NameProceedings of the IEEE Radar Conference
ISSN (Print)1097-5764
ISSN (Electronic)2375-5318


Conference2023 IEEE International Radar Conference, RADAR 2023


  • NLOS operation
  • Surface reconstruction
  • multi-path mitigation
  • neural network-based radar processing
  • wall reconstruction

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Instrumentation


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