Performance Comparison of Moving Target Classification based on Deep Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hur, Jun | - |
dc.contributor.author | Nam, Haewoon | - |
dc.date.accessioned | 2023-08-16T07:30:29Z | - |
dc.date.available | 2023-08-16T07:30:29Z | - |
dc.date.issued | 2022-10 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/113749 | - |
dc.description.abstract | Radar target detection is a basic but important process of radar systems, and it is difficult to distinguish and measure targets in real-world environments. Therefore, distinguishing between humans and animals based on radar signals is a difficult task in the field of ground radar. The radar signal processing section uses the in-phase/quadraturephase (I/Q) matrix radar signal data and geolocation types as inputs and performs binary classification to classify animals and humans. In this radar signal processing, deep learning methods are adopted as feasible solutions. However, there is a limited lack of training data in the real world and a problem with jamming signals, which are adversarial attacks. However, it is difficult to collect a lot of training data in a real-time environment. Reflecting this, we learn only some data from MAFAT Radar Challenge data to compare and analyze the classification performance of conventional methods convolutional neural network (CNN) and CNN-based U-Net and U-Net with residual blocks U-Net (ResUNet) algorithms | - |
dc.format.extent | 3 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE | - |
dc.title | Performance Comparison of Moving Target Classification based on Deep Learning | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/ICTC55196.2022.9952676 | - |
dc.identifier.bibliographicCitation | 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), pp 1533 - 1535 | - |
dc.citation.title | 2022 13th International Conference on Information and Communication Technology Convergence (ICTC) | - |
dc.citation.startPage | 1533 | - |
dc.citation.endPage | 1535 | - |
dc.type.docType | Proceeding | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | other | - |
dc.subject.keywordAuthor | Classification | - |
dc.subject.keywordAuthor | CNN | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Radar target detection | - |
dc.subject.keywordAuthor | Res-UNet | - |
dc.subject.keywordAuthor | U-Net | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9952676?arnumber=9952676&SID=EBSCO:edseee | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG 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.