GAN-CRT: A Novel Range-Doppler Estimation Method in Automotive Radar Systems

Yun Han Pan, Chia Hung Lin, Ta Sung Lee

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

Abstract

In automotive radar systems, the range and Doppler velocity of vehicles surrounding the radars can be estimated by performing a fast Fourier transform (FFT) on the processed received signals reflected by the vehicles. The trade-off between unambiguity for estimation and resolution in FFT-based estimation methods can be broken with low computational complexity by introducing the Chinese remainder theorem (CRT). However, there are two challenges in CRT-based methods: the additional target association procedure and the error propagation drawback. In this study, a novel multi-waveform radar frame structure is proposed to facilitate the use of the CRT. Based on the frame structure, a corresponding CRT-based target association method is proposed to eliminate ghost targets. Moreover, a generative adversarial neural network (GAN)-based target association method is proposed to further address the error propagation drawback. Simulation results show the robustness of the GAN-based method, with an outstanding performance compared to other rule-based methods, even in severe error scenarios.

Original languageEnglish
Title of host publication2020 IEEE 91st Vehicular Technology Conference, VTC Spring 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728152073
DOIs
StatePublished - May 2020
Event91st IEEE Vehicular Technology Conference, VTC Spring 2020 - Antwerp, Belgium
Duration: 25 May 202028 May 2020

Publication series

NameIEEE Vehicular Technology Conference
Volume2020-May
ISSN (Print)1550-2252

Conference

Conference91st IEEE Vehicular Technology Conference, VTC Spring 2020
CountryBelgium
CityAntwerp
Period25/05/2028/05/20

Keywords

  • automotive radar
  • CRT
  • deep learning
  • FFT
  • GAN
  • multi-waveform
  • range-Doppler estimation
  • target association

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