DoA Estimation for FMCW Radar by 3D-CNN.

Tzu-Hsien Sang, Feng-Tsun Chien, Chia-Chih Chang, Kuan-Yu Tseng, Bo-Sheng Wang, Jiun-In Guo

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Pedestrian Traffic Light Control with Crosswalk FMCW Radar and Group Tracking Algorithm.Sensors (Basel, Switzerland) · 2022
Article

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5 · Who and what money

Authors and funding

6 authors.

Tzu-Hsien SangInstitute of Electronics, National Yang Ming Chiao Tung University, Hsin-Chu 300, Taiwan.ORCID 0000-0003-3041-3394

Feng-Tsun ChienInstitute of Electronics, National Yang Ming Chiao Tung University, Hsin-Chu 300, Taiwan.

Chia-Chih ChangInstitute of Electronics, National Yang Ming Chiao Tung University, Hsin-Chu 300, Taiwan.

Kuan-Yu TsengInstitute of Electronics, National Yang Ming Chiao Tung University, Hsin-Chu 300, Taiwan.ORCID 0000-0002-9894-3191

Bo-Sheng WangInstitute of Electronics, National Chiao Tung University, Hsin-Chu 300, Taiwan.

Jiun-In GuoInstitute of Electronics, National Yang Ming Chiao Tung University, Hsin-Chu 300, Taiwan.ORCID 0000-0003-0402-2621

Funding

Ministry of Education Higher Education Sprout ProjectMinistry of Science and Technology, Taiwan MOST 108-3017-F-009-001Ministry of Science and Technology, Taiwan MOST 110-2634-F-009-028

6 · The paper itself

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.

Indexed as

deep learningdirection-of-arrival estimationFMCW radarthree-dimension convolution network

Identifiers

PMID34450765

DOI10.3390/s21165319

PMCPMC8399740

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.