TY - GEN
T1 - Moving target classification in automotive radar systems using convolutional recurrent neural networks
AU - Kim, Sangtae
AU - Lee, Seunghwan
AU - Doo, Seungho
AU - Shim, Byonghyo
N1 - Funding Information:
This work was partly supported by Hyundai Mobis Co. and the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (2016R1A2B3015576)
Publisher Copyright:
© EURASIP 2018.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018/11/29
Y1 - 2018/11/29
N2 - Moving target classification is a key ingredient to avoid accident in autonomous driving systems. Recently, fast chirp frequency modulated continuous wave (FMCW) radar has been popularly used to recognize moving targets due to its ability to discriminate moving objects and stationary clutter. In order to protect vulnerable road users such as pedestrians and cyclists, it is essential to identify road users in a very short period of time. In this paper, we propose a deep neural network that consists of convolutional recurrent units for target classification in automotive radar system. In our experiment, using the real data measured by the fast chirp FMCW-based high range resolution radar, we show that the proposed network is capable of learning the dynamics in time-series image data and outperforms the conventional classification schemes.
AB - Moving target classification is a key ingredient to avoid accident in autonomous driving systems. Recently, fast chirp frequency modulated continuous wave (FMCW) radar has been popularly used to recognize moving targets due to its ability to discriminate moving objects and stationary clutter. In order to protect vulnerable road users such as pedestrians and cyclists, it is essential to identify road users in a very short period of time. In this paper, we propose a deep neural network that consists of convolutional recurrent units for target classification in automotive radar system. In our experiment, using the real data measured by the fast chirp FMCW-based high range resolution radar, we show that the proposed network is capable of learning the dynamics in time-series image data and outperforms the conventional classification schemes.
KW - Classification
KW - Fast chirp FMCW radar
KW - Recurrent neural networks
KW - convolutional neural networks
UR - http://www.scopus.com/inward/record.url?scp=85059818371&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059818371&partnerID=8YFLogxK
U2 - 10.23919/EUSIPCO.2018.8553185
DO - 10.23919/EUSIPCO.2018.8553185
M3 - Conference contribution
AN - SCOPUS:85059818371
T3 - European Signal Processing Conference
SP - 1482
EP - 1486
BT - 2018 26th European Signal Processing Conference, EUSIPCO 2018
PB - European Signal Processing Conference, EUSIPCO
T2 - 26th European Signal Processing Conference, EUSIPCO 2018
Y2 - 3 September 2018 through 7 September 2018
ER -