Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 남해운 | - |
dc.date.accessioned | 2022-04-20T01:44:40Z | - |
dc.date.available | 2022-04-20T01:44:40Z | - |
dc.date.issued | 2021-10 | - |
dc.identifier.citation | 2021 International Conference on Information and Communication Technology Convergence (ICTC) Information and Communication Technology Convergence (ICTC), 2021 International Conference on. :1486-1489 Oct, 2021 | en_US |
dc.identifier.isbn | 978-1-6654-2383-0 | - |
dc.identifier.issn | 2162-1233 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/170156 | - |
dc.identifier.uri | https://ieeexplore.ieee.org/document/9620795?arnumber=9620795&SID=EBSCO:edseee | - |
dc.description.abstract | For indoor positioning, it is important to accurately calculate inter-node distances, in which identifying whether the channel environment is line-of-sight (LOS) or non-LOS (NLOS) condition is critical. The traditional methods for NLOS detection often use extracting features of the channel environment. However, machine learning has recently known to make it possible to identify the channel environment more accurately than traditional methods. Therefore, we compare the performance of feature extraction-based SVM model for NLOS detection and CNN model based on imaging algorithms. Experiments show that CNN classifiers provide higher classification accuracy than SVM classifiers. In addition, it shows that applying imaging algorithms to data further improves the performance of CNN classifiers. | en_US |
dc.description.sponsorship | This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT). (No. 2019M3F6A110610813) | en_US |
dc.language.iso | en | en_US |
dc.publisher | 통신학회 | en_US |
dc.subject | Bioengineering | en_US |
dc.subject | Communication, Networking and Broadcast Technologies | en_US |
dc.subject | Components, Circuits, Devices and Systems | en_US |
dc.subject | Computing and Processing | en_US |
dc.subject | Fields, Waves and Electromagnetics | en_US |
dc.subject | Power, Energy and Industry Applications | en_US |
dc.subject | Signal Processing and Analysis | en_US |
dc.subject | Transportation | en_US |
dc.subject | Support vector machines | en_US |
dc.subject | Machine learning algorithms | en_US |
dc.subject | Imaging | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Feature extraction | en_US |
dc.subject | Classification algorithms | en_US |
dc.subject | Information and communication technology | en_US |
dc.subject | UWB | en_US |
dc.subject | NLOS detection | en_US |
dc.subject | imaging | en_US |
dc.subject | SVM | en_US |
dc.subject | CNN | en_US |
dc.title | Performance Comparison of NLOS Detection Methods in UWB | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/ICTC52510.2021.9620795 | - |
dc.relation.page | 1486-1489 | - |
dc.contributor.googleauthor | Yoon, Jaehyeok | - |
dc.contributor.googleauthor | Kim, Hyeongyun | - |
dc.contributor.googleauthor | Seo, Dongho | - |
dc.contributor.googleauthor | Nam, Haewoon | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | SCHOOL OF ELECTRICAL ENGINEERING | - |
dc.identifier.pid | hnam | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.