@article{4863095f1057417c8f734cac7b58d757,
title = "DoA estimation for FMCW radar by 3D-CNN",
abstract = "A method of direction-of-arrival (DoA) estimation for FMCW (Frequency Modulated Continuous Wave) radar is presented. In addition to MUSIC, which is the popular high-resolution DoA estimation algorithm, 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 experiments are provided to demonstrate the superior performance, as well as the limitations, of the proposed 3D-CNN.",
keywords = "Deep learning, Direction-of-arrival estimation, FMCW radar, Three-dimension convolution network",
author = "Tzu-Hsien Sang and Feng-Tsun Chien and Chang, {Chia Chih} and Tseng, {Kuan Yu} and Wang, {Bo Sheng} and Jiun-In Guo",
note = "Funding Information: Funding: This work is partially supported by the “Center for mmWave Smart Radar Systems and Technologies” under the “Featured Areas Research Center Program” within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE), Taiwan, R.O.C., and partially supported under MOST projects with grants MOST 108-3017-F-009-001 and MOST 110-2634-F-009-028. Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",
year = "2021",
month = aug,
doi = "10.3390/s21165319",
language = "American English",
volume = "21",
journal = "Sensors",
issn = "1424-8220",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "16",
}