Multi-input deep learning based fmcw radar signal classification

Daewoong Cha, Sohee Jeong, Minwoo Yoo, Jiyong Oh, Dongseog Han

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

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.

Original languageEnglish
Article number1144
JournalElectronics (Switzerland)
Volume10
Issue number10
DOIs
StatePublished - 2 May 2021

Keywords

  • Classification
  • Deep learning
  • Frequency modulated continuous wave (FMCW) radar

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