Deep Learning-Based Range-Doppler Map Reconstruction in Automotive Radar Systems

Hao Wei Hsu, Yu Chien Lin, Ming-Chun Lee, Chia Hung Lin, Ta-Sung Lee

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

In this paper, we consider the automotive orthogonal frequency division modulation-radar in millimeter wave band. To avoid interference between different radar systems, resources need to be split and then used by different radar systems. This thus degrades the radar performance as compared to the radar system having full resources (FRs). To mitigate this issue, we develop a deep learning-based range-Doppler (R-D) map reconstruction approach along with a time-frequency resource allocation scheme. In the reconstruction approach, we propose a deep learning-based convolutional neural network to reconstruct the R-D map such that the reconstructed R-D map can be close to the R-D map under FRs. In the resource allocation scheme, we propose a block-wise interleaved method that can facilitate the proposed reconstruction approach. Simulation results show that our proposed approach can effectively mitigate the performance degradation of radar systems when resources are shared among users.

原文English
主出版物標題2021 IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁數7
ISBN(電子)9781728189642
DOIs
出版狀態Published - 25 4月 2021
事件93rd IEEE Vehicular Technology Conference, VTC 2021-Spring - Virtual, Online
持續時間: 25 4月 202128 4月 2021

出版系列

名字IEEE Vehicular Technology Conference
2021-April
ISSN(列印)1550-2252

Conference

Conference93rd IEEE Vehicular Technology Conference, VTC 2021-Spring
城市Virtual, Online
期間25/04/2128/04/21

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