Working Paper Article Version 1 This version is not peer-reviewed

DoA Estimation for FMCW Radar by 3D-CNN

Version 1 : Received: 11 June 2021 / Approved: 14 June 2021 / Online: 14 June 2021 (16:54:32 CEST)

How to cite: Sang, T.; Chien, F.; Chang, C.; Tseng, K.; Wang, B.; Guo, J. DoA Estimation for FMCW Radar by 3D-CNN. Preprints 2021, 2021060382 Sang, T.; Chien, F.; Chang, C.; Tseng, K.; Wang, B.; Guo, J. DoA Estimation for FMCW Radar by 3D-CNN. Preprints 2021, 2021060382

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.

Subject Areas

FMCW Radar; Deep Learning; Three-Dimension Convolution Network; Direction-of-Arrival Estimation

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