Multi-Input Deep Learning Based FMCW Radar Signal Classification

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 le...

Full description

Saved in:
Bibliographic Details
Published inElectronics (Basel) Vol. 10; no. 10; p. 1144
Main Authors Cha, Daewoong, Jeong, Sohee, Yoo, Minwoo, Oh, Jiyong, Han, Dongseog
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 12.05.2021
Subjects
Accident prevention
Accuracy
Algorithms
Artificial neural networks
Braking
Braking systems
Cameras
Classification
Classifiers
Continuous radiation
Deep learning
Emergency vehicles
Machine learning
Neural networks
Noise
Radar
Radar data
Sensors
Signal classification
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics10101144