Moving target classification in automotive radar systems using convolutional recurrent neural networks

Sangtae Kim, Seunghwan Lee, Seungho Doo, Byonghyo Shim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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 languageEnglish
Title of host publication2018 26th European Signal Processing Conference, EUSIPCO 2018
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages1482-1486
Number of pages5
Volume2018-September
ISBN (Electronic)9789082797015
DOIs
Publication statusPublished - 2018 Nov 29
Event26th European Signal Processing Conference, EUSIPCO 2018 - Rome, Italy
Duration: 2018 Sep 32018 Sep 7

Other

Other26th European Signal Processing Conference, EUSIPCO 2018
CountryItaly
CityRome
Period18/9/318/9/7

Fingerprint

Recurrent neural networks
Radar systems
Continuous wave radar
Time series
Accidents
Radar
Experiments

Keywords

  • Classification
  • convolutional neural networks
  • Fast chirp FMCW radar
  • Recurrent neural networks

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Kim, S., Lee, S., Doo, S., & Shim, B. (2018). Moving target classification in automotive radar systems using convolutional recurrent neural networks. In 2018 26th European Signal Processing Conference, EUSIPCO 2018 (Vol. 2018-September, pp. 1482-1486). [8553185] European Signal Processing Conference, EUSIPCO. https://doi.org/10.23919/EUSIPCO.2018.8553185

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 proceedingConference contribution

Kim, S, Lee, S, Doo, S & Shim, B 2018, Moving target classification in automotive radar systems using convolutional recurrent neural networks. in 2018 26th European Signal Processing Conference, EUSIPCO 2018. vol. 2018-September, 8553185, European Signal Processing Conference, EUSIPCO, pp. 1482-1486, 26th European Signal Processing Conference, EUSIPCO 2018, Rome, Italy, 18/9/3. https://doi.org/10.23919/EUSIPCO.2018.8553185
Kim S, Lee S, Doo S, Shim B. Moving target classification in automotive radar systems using convolutional recurrent neural networks. In 2018 26th European Signal Processing Conference, EUSIPCO 2018. Vol. 2018-September. European Signal Processing Conference, EUSIPCO. 2018. p. 1482-1486. 8553185 https://doi.org/10.23919/EUSIPCO.2018.8553185
Kim, Sangtae ; Lee, Seunghwan ; Doo, Seungho ; Shim, Byonghyo. / Moving target classification in automotive radar systems using convolutional recurrent neural networks. 2018 26th European Signal Processing Conference, EUSIPCO 2018. Vol. 2018-September European Signal Processing Conference, EUSIPCO, 2018. pp. 1482-1486
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