Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 남해운 | - |
dc.date.accessioned | 2024-06-14T04:37:29Z | - |
dc.date.available | 2024-06-14T04:37:29Z | - |
dc.date.issued | 2023-10 | - |
dc.identifier.citation | 2023 14th International Conference on Information and Communication Technology Convergence (ICTC), page. 1721-1724 | en_US |
dc.identifier.issn | 2162-1241 | en_US |
dc.identifier.issn | 2162-1233 | en_US |
dc.identifier.uri | https://ieeexplore.ieee.org/abstract/document/10393280 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/190724 | - |
dc.description.abstract | Low Probability of Intercept (LPI) radar signals play a vital role in electronic warfare by maintaining informational superiority. Classifying these LPI radar waveforms is a key capability but remains a challenging task due to strong noise interference. Traditional signal processing techniques often show limitations in effectively removing complex noise signals. While deep learning-based modulation classification has exhibited superior performance, its effectiveness is compromised in the presence of significant noise. In this study, we propose a deep learning-based denoising method using the U²-Net for LPI radar signals, followed by modulation classification using a Convolutional Neural Network (CNN). We further compare the performance of U²-Net with other denoising models such as UNet and denoising autoencoder. Experimental results demonstrate that the U²-Net outperforms other methods, achieving over 90% classification accuracy for signals with a signal-to-noise ratio above -14dB | en_US |
dc.description.sponsorship | This work was supported by the Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (2019-0-00964, Development of Incumbent Radio Stations Protection and Frequency Sharing Technology through Spectrum Challenge) | en_US |
dc.language | en_US | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartofseries | ;1721-1724 | - |
dc.subject | Low Probability of Intercept (LPI) radar | en_US |
dc.subject | time frequency analysis | en_US |
dc.subject | U²-Net | en_US |
dc.subject | U-Net | en_US |
dc.subject | denoising autoencoder | en_US |
dc.title | LPI radar signal recognition with U?Net-based denoising | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/ICTC58733.2023.10393280 | en_US |
dc.relation.page | 1-2 | - |
dc.contributor.googleauthor | Lee, Siho | - |
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 | - |
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