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Article

Multi-Input Deep Learning Based FMCW Radar Signal Classification

1
School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Korea
2
Daegu-Gyeongbuk Research Center, Electronics and Telecommunications Research Institute (ETRI), 1, Techno sunhwan-ro 10-gil Yuga-eup, Dalseong-gun, Daegu 42994, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Jose Eugenio Naranjo
Electronics 2021, 10(10), 1144; https://doi.org/10.3390/electronics10101144
Received: 19 April 2021 / Revised: 8 May 2021 / Accepted: 10 May 2021 / Published: 12 May 2021
(This article belongs to the Special Issue AI-Based Autonomous Driving System)
In autonomous driving vehicles, the emergency braking system uses lidar or radar sensors to recognize the surrounding environment and prevent accidents. The conventional classifiers based on radar data using deep learning are single input structures using range–Doppler maps or micro-Doppler. Deep learning with a single input structure has limitations in improving classification performance. In this paper, we propose a multi-input classifier based on convolutional neural network (CNN) to reduce the amount of computation and improve the classification performance using the frequency modulated continuous wave (FMCW) radar. The proposed multi-input deep learning structure is a CNN-based structure using a distance Doppler map and a point cloud map as multiple inputs. The classification accuracy with the range–Doppler map or the point cloud map is 85% and 92%, respectively. It has been improved to 96% with both maps. View Full-Text
Keywords: frequency modulated continuous wave (FMCW) radar; deep learning; classification frequency modulated continuous wave (FMCW) radar; deep learning; classification
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MDPI and ACS Style

Cha, D.; Jeong, S.; Yoo, M.; Oh, J.; Han, D. Multi-Input Deep Learning Based FMCW Radar Signal Classification. Electronics 2021, 10, 1144. https://doi.org/10.3390/electronics10101144

AMA Style

Cha D, Jeong S, Yoo M, Oh J, Han D. Multi-Input Deep Learning Based FMCW Radar Signal Classification. Electronics. 2021; 10(10):1144. https://doi.org/10.3390/electronics10101144

Chicago/Turabian Style

Cha, Daewoong, Sohee Jeong, Minwoo Yoo, Jiyong Oh, and Dongseog Han. 2021. "Multi-Input Deep Learning Based FMCW Radar Signal Classification" Electronics 10, no. 10: 1144. https://doi.org/10.3390/electronics10101144

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