상세정보
학술지
Multi-Input Deep Learning Based FMCW Radar Signal Classification
- 저자
- 차대웅, 정소희, 유민우, 오지용, 한동석
- 발행일
- 202105
- 출처
- Electronics, v.10 no.10, pp.1-11
- ISSN
- 2079-9292
- 출판사
- MDPI
- 협약과제
- 21ZD1100, 대경권 지역산업 기반 ICT 융합기술 고도화 지원사업, 문기영
- 초록
- 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?밆oppler 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?밆oppler map or the point cloud map is 85% and 92%, respectively. It has been improved to 96% with both maps.
- KSP 제안 키워드
- Classification Performance, Convolution neural network(CNN), Emergency Braking, Frequency modulated continuous wave (FMCW) radar, Micro-Doppler, Multi-input, Point Cloud Map, Radar Data, Radar sensor, Radar signal classification, Single-input
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