Deep Learning-Based Angular Resolution Improvement in Planar Sensor Array
DC Field | Value | Language |
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dc.contributor.author | Jeong, Taewon | - |
dc.contributor.author | Kang, Sung-Wook | - |
dc.contributor.author | Lee, Seongwook | - |
dc.date.accessioned | 2024-01-09T17:03:08Z | - |
dc.date.available | 2024-01-09T17:03:08Z | - |
dc.date.issued | 2023-12 | - |
dc.identifier.issn | 2475-1472 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/70857 | - |
dc.description.abstract | The high angular resolution, which leads to an accurate direction-of-arrival (DOA) estimation, is essential in the radar systems for target detection and localization. Therefore, we propose a generative adversarial network (GAN)-based method that improves the angular resolution in target detection images. In the proposed network, we use the U-Net and the patch discriminator as the generator and the discriminator, respectively. Then, we verify the performance of the proposed method through simulations. The mean-squared error between the image generated by the proposed deep learning network and the ground truth image is 0.004, indicating a high level of similarity. In addition, the peak signal-to-noise ratio of the image with the increased resolution is about 11 dB higher than that of the original low-resolution (LR) image. By enhancing the angular resolution through the proposed method, the accuracy of DOA estimation can be improved in radar systems. © 2017 IEEE. | - |
dc.format.extent | 4 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Deep Learning-Based Angular Resolution Improvement in Planar Sensor Array | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/LSENS.2023.3330101 | - |
dc.identifier.bibliographicCitation | IEEE Sensors Letters, v.7, no.12, pp 1 - 4 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 001105613800002 | - |
dc.identifier.scopusid | 2-s2.0-85177179838 | - |
dc.citation.endPage | 4 | - |
dc.citation.number | 12 | - |
dc.citation.startPage | 1 | - |
dc.citation.title | IEEE Sensors Letters | - |
dc.citation.volume | 7 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | direction-of-arrival | - |
dc.subject.keywordAuthor | DOA | - |
dc.subject.keywordAuthor | generative adversarial network | - |
dc.subject.keywordAuthor | GAN | - |
dc.subject.keywordAuthor | planar sensor array | - |
dc.subject.keywordAuthor | resolution improvement | - |
dc.subject.keywordAuthor | Sensor signal processing | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | esci | - |
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