RFDOA-Net: An Efficient ConvNet for RF-Based DOA Estimation in UAV Surveillance Systems
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Akter, Rubina | - |
dc.contributor.author | Doan, Van-Sang | - |
dc.contributor.author | Huynh-The, Thien | - |
dc.contributor.author | Kim, Dong-Seong | - |
dc.date.accessioned | 2021-12-22T00:40:11Z | - |
dc.date.available | 2021-12-22T00:40:11Z | - |
dc.date.created | 2021-12-21 | - |
dc.date.issued | 2021-11 | - |
dc.identifier.issn | 0018-9545 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/20327 | - |
dc.description.abstract | This paper presents a convolution neural network (CNN)-based direction of arrival (DOA) estimation method for radio frequency (RF) signals acquired by a nonuniform linear antenna array (NULA) in unmanned aerial vehicle (UAV) localization systems. The proposed deep CNN, namely RFDOA-Net, is designed with three primary processing modules, such as collective feature extraction, multi-scaling feature processing, and complexity-accuracy trade-off, to learn the multi-scale intrinsic characteristics for multi-class angle classification. In several specific modules, the regular convolutional and grouped convolutional layers are leveraged with different filter sizes to enrich diversified features and reduce network complexity besides adopting residual connection to prevent vanishing gradient. For performance evaluation, we generate a synthetic signal dataset for DOA estimation under the multipath propagation channel with the presence of additive noise, propagation attenuation and delay. In simulations, the effectiveness of RFDOA-Net is investigated comprehensively with various processing modules and antenna configurations. Compared with several state-of-the-art deep learning-based models, RFDOA-Net shows the superiority in terms of accuracy with over 94% accuracy at 5 dB signal-to-noise ratio (SNR) with cost-efficiency. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | RFDOA-Net: An Efficient ConvNet for RF-Based DOA Estimation in UAV Surveillance Systems | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Akter, Rubina | - |
dc.contributor.affiliatedAuthor | Huynh-The, Thien | - |
dc.contributor.affiliatedAuthor | Kim, Dong-Seong | - |
dc.identifier.doi | 10.1109/TVT.2021.3114058 | - |
dc.identifier.wosid | 000720520400085 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, v.70, no.11, pp.12209 - 12214 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | - |
dc.citation.title | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | - |
dc.citation.volume | 70 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 12209 | - |
dc.citation.endPage | 12214 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalResearchArea | Transportation | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Transportation Science & Technology | - |
dc.subject.keywordPlus | NETWORKS | - |
dc.subject.keywordAuthor | Direction-of-arrival estimation | - |
dc.subject.keywordAuthor | Estimation | - |
dc.subject.keywordAuthor | Feature extraction | - |
dc.subject.keywordAuthor | Array signal processing | - |
dc.subject.keywordAuthor | Linear antenna arrays | - |
dc.subject.keywordAuthor | Convolution | - |
dc.subject.keywordAuthor | Antenna arrays | - |
dc.subject.keywordAuthor | Convolution neural network | - |
dc.subject.keywordAuthor | direction of arrival estimation | - |
dc.subject.keywordAuthor | nonuniform linear antenna array | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
350-27, Gumi-daero, Gumi-si, Gyeongsangbuk-do, Republic of Korea (39253)054-478-7170
COPYRIGHT 2020 Kumoh University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.