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

Yun Han Pan, Chia Hung Lin, Ta Sung Lee

研究成果: Conference contribution同行評審

3 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2020 IEEE 91st Vehicular Technology Conference, VTC Spring 2020 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728152073
DOIs
出版狀態Published - 5月 2020
事件91st IEEE Vehicular Technology Conference, VTC Spring 2020 - Antwerp, Belgium
持續時間: 25 5月 202028 5月 2020

出版系列

名字IEEE Vehicular Technology Conference
2020-May
ISSN(列印)1550-2252

Conference

Conference91st IEEE Vehicular Technology Conference, VTC Spring 2020
國家/地區Belgium
城市Antwerp
期間25/05/2028/05/20

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