High Fidelity Physics Simulation-Based Convolutional Neural Network for Automotive Radar Target Classification Using Micro-Doppler

High Fidelity Physics Simulation-Based Convolutional Neural Network for Automotive Radar Target Classification Using Micro-Doppler

Detection and classification of vulnerable road users (VRUs) such as pedestrians and cyclists is a key requirement for the realization of fully autonomous vehicles. Radar-based classification of VRUs can be achieved by exploiting differences in the micro-Doppler signatures associated with VRUs. Spec...

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Bibliographic Details
Main Authors: Ushemadzoro Chipengo, Arien P. Sligar, Stefano Mihai Canta, Markus Goldgruber, Hen Leibovich, Shawn Carpenter
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Automotive radar
micro-Doppler
machine learning
convolutional neural networks
FMCW
simulation
Online Access:https://ieeexplore.ieee.org/document/9446140/

Internet

https://ieeexplore.ieee.org/document/9446140/

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