Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 2018 26th European Signal Processing Conference, EUSIPCO 2018 |
| Publisher | European Signal Processing Conference, EUSIPCO |
| Pages | 1482-1486 |
| Number of pages | 5 |
| Volume | 2018-September |
| ISBN (Electronic) | 9789082797015 |
| DOIs | |
| Publication status | Published - 2018 Nov 29 |
| Event | 26th European Signal Processing Conference, EUSIPCO 2018 - Rome, Italy Duration: 2018 Sep 3 → 2018 Sep 7 |
Other
| Other | 26th European Signal Processing Conference, EUSIPCO 2018 |
|---|---|
| Country | Italy |
| City | Rome |
| Period | 18/9/3 → 18/9/7 |
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Keywords
- Classification
- convolutional neural networks
- Fast chirp FMCW radar
- Recurrent neural networks
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering
Cite this
Moving target classification in automotive radar systems using convolutional recurrent neural networks. / Kim, Sangtae; Lee, Seunghwan; Doo, Seungho; Shim, Byonghyo.
2018 26th European Signal Processing Conference, EUSIPCO 2018. Vol. 2018-September European Signal Processing Conference, EUSIPCO, 2018. p. 1482-1486 8553185.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
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 - convolutional neural networks
KW - Fast chirp FMCW radar
KW - Recurrent 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
VL - 2018-September
SP - 1482
EP - 1486
BT - 2018 26th European Signal Processing Conference, EUSIPCO 2018
PB - European Signal Processing Conference, EUSIPCO
ER -