Automatic Recognition for LPI Radar Waveform Using Sequence-based Deep Learning Model
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Dongeun | - |
dc.contributor.author | Kim, Yoonji | - |
dc.contributor.author | Seo, Dongho | - |
dc.contributor.author | Lee, Wonjin | - |
dc.contributor.author | Yoon, Dongweon | - |
dc.date.accessioned | 2023-09-04T05:33:11Z | - |
dc.date.available | 2023-09-04T05:33:11Z | - |
dc.date.created | 2023-08-29 | - |
dc.date.issued | 2023-07 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/189609 | - |
dc.description.abstract | Automatic recognition of low probability of intercept (LPI) radar waveforms is an essential technology in contemporary electromagnetic warfare systems. In recent years, sequence-based deep learning (DL) models have been proposed for the recognition of LPI radar waveforms, which can effectively recognize radar waveforms in rapidly changing environments. In this paper, we propose a sequence-based DL model for recognizing 12 different kinds of LPI radar waveforms. The proposed model is based on a residual network (ResNet), which can extract rich features through its deep structure. For inputs of the model, the discrete Fourier transform (DFT) and autocorrelation function (ACF) of the radar waveform are considered. Simulation results show that the proposed model outperforms existing sequence-based DL models. It is also shown that utilizing the DFT as the model's input provides an advantage over utilizing ACF in terms of recognition performance. © 2023 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Automatic Recognition for LPI Radar Waveform Using Sequence-based Deep Learning Model | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Yoon, Dongweon | - |
dc.identifier.doi | 10.1109/CITS58301.2023.10188795 | - |
dc.identifier.scopusid | 2-s2.0-85167865858 | - |
dc.identifier.bibliographicCitation | Proceedings of the 2023 IEEE International Conference on Computer, Information, and Telecommunication Systems, CITS 2023, pp.1 - 4 | - |
dc.relation.isPartOf | Proceedings of the 2023 IEEE International Conference on Computer, Information, and Telecommunication Systems, CITS 2023 | - |
dc.citation.title | Proceedings of the 2023 IEEE International Conference on Computer, Information, and Telecommunication Systems, CITS 2023 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 4 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Electronic warfare | - |
dc.subject.keywordPlus | Learning systems | - |
dc.subject.keywordPlus | Military applications | - |
dc.subject.keywordPlus | Discrete Fourier transforms | - |
dc.subject.keywordPlus | Autocorrelation functions | - |
dc.subject.keywordPlus | Automatic recognition | - |
dc.subject.keywordPlus | Changing environment | - |
dc.subject.keywordPlus | Electromagnetics | - |
dc.subject.keywordPlus | Learning models | - |
dc.subject.keywordPlus | Low probability of intercept | - |
dc.subject.keywordPlus | Radar waveform recognition | - |
dc.subject.keywordPlus | Radar waveforms | - |
dc.subject.keywordPlus | Residual network | - |
dc.subject.keywordPlus | Sequence-based deep learning model | - |
dc.subject.keywordAuthor | low probability of intercept | - |
dc.subject.keywordAuthor | Radar waveform recognition | - |
dc.subject.keywordAuthor | ResNet | - |
dc.subject.keywordAuthor | sequence-based deep learning model | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/10188795 | - |
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