Sang, T.-H.; Chien, F.-T.; Chang, C.-C.; Tseng, K.-Y.; Wang, B.-S.; Guo, J.-I. DoA Estimation for FMCW Radar by 3D-CNN. Sensors2021, 21, 5319.
Sang, T.-H.; Chien, F.-T.; Chang, C.-C.; Tseng, K.-Y.; Wang, B.-S.; Guo, J.-I. DoA Estimation for FMCW Radar by 3D-CNN. Sensors 2021, 21, 5319.
Sang, T.-H.; Chien, F.-T.; Chang, C.-C.; Tseng, K.-Y.; Wang, B.-S.; Guo, J.-I. DoA Estimation for FMCW Radar by 3D-CNN. Sensors2021, 21, 5319.
Sang, T.-H.; Chien, F.-T.; Chang, C.-C.; Tseng, K.-Y.; Wang, B.-S.; Guo, J.-I. DoA Estimation for FMCW Radar by 3D-CNN. Sensors 2021, 21, 5319.
Abstract
A method of direction-of-arrival (DoA) estimation for FMCW radars is presented. In addition to MUSIC and ESPRIT, which are well-known high-resolution DoA estimation algorithms, deep learning has recently emerged as a very promising alternative. It is proposed in this paper to use a 3D convolutional neural network (CNN) for DoA estimation. The 3D-CNN extracts from the radar data cube spectrum features of the region of interest (RoI) centered on the potential positions of the targets, thereby capturing the spectrum phase shift information, which corresponds to DoA, along the antenna axis. Finally, the results of simulations and field experiments are provided to demonstrate the superior performance, as well as the limitations, of the proposed 3D-CNN.
Keywords
FMCW Radar; Deep Learning; Three-Dimension Convolution Network; Direction-of-Arrival Estimation
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
