Unmanned Aerial Vehicle Recognition Based on Clustering by Fast Search and Find of Density Peaks (CFSFDP) with Polarimetric Decomposition
Unmanned aerial vehicles (UAV) have become vital targets in civilian and military fields. However, the polarization characteristics are rarely studied. This paper studies the polarization property of UAVs via the fusion of three polarimetric decomposition methods. A novel algorithm is presented to c...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-12-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/7/12/364 |
| _version_ | 1798025462403825664 |
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| author | Hao Wu Bo Pang Dahai Dai Jiani Wu Xuesong Wang |
| author_facet | Hao Wu Bo Pang Dahai Dai Jiani Wu Xuesong Wang |
| author_sort | Hao Wu |
| collection | DOAJ |
| description | Unmanned aerial vehicles (UAV) have become vital targets in civilian and military fields. However, the polarization characteristics are rarely studied. This paper studies the polarization property of UAVs via the fusion of three polarimetric decomposition methods. A novel algorithm is presented to classify and recognize UAVs automatically which includes a clustering method proposed in “<i>Science</i>„, one of the top journals in academia. Firstly, the selection of the imaging algorithm ensures the quality of the radar images. Secondly, local geometrical structures of UAVs can be extracted based on Pauli, Krogager, and Cameron polarimetric decomposition. Finally, the proposed algorithm with clustering by fast search and find of density peaks (CFSFDP) has been demonstrated to be better than the original methods under the various noise conditions with the fusion of three polarimetric decomposition methods. |
| first_indexed | 2024-04-11T18:19:25Z |
| format | Article |
| id | doaj.art-ec7e4d3ca3694caba3109a6bba98b8f3 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-11T18:19:25Z |
| publishDate | 2018-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-ec7e4d3ca3694caba3109a6bba98b8f32022-12-22T04:09:48ZengMDPI AGElectronics2079-92922018-12-0171236410.3390/electronics7120364electronics7120364Unmanned Aerial Vehicle Recognition Based on Clustering by Fast Search and Find of Density Peaks (CFSFDP) with Polarimetric DecompositionHao Wu0Bo Pang1Dahai Dai2Jiani Wu3Xuesong Wang4State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, ChinaState Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, ChinaState Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, ChinaState Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, ChinaState Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, ChinaUnmanned aerial vehicles (UAV) have become vital targets in civilian and military fields. However, the polarization characteristics are rarely studied. This paper studies the polarization property of UAVs via the fusion of three polarimetric decomposition methods. A novel algorithm is presented to classify and recognize UAVs automatically which includes a clustering method proposed in “<i>Science</i>„, one of the top journals in academia. Firstly, the selection of the imaging algorithm ensures the quality of the radar images. Secondly, local geometrical structures of UAVs can be extracted based on Pauli, Krogager, and Cameron polarimetric decomposition. Finally, the proposed algorithm with clustering by fast search and find of density peaks (CFSFDP) has been demonstrated to be better than the original methods under the various noise conditions with the fusion of three polarimetric decomposition methods.https://www.mdpi.com/2079-9292/7/12/364unmanned aerial vehicleclustering methodsman-made targetssynthetic aperture radar (SAR)inverse synthetic aperture radar (ISAR)polarimetric decomposition |
| spellingShingle | Hao Wu Bo Pang Dahai Dai Jiani Wu Xuesong Wang Unmanned Aerial Vehicle Recognition Based on Clustering by Fast Search and Find of Density Peaks (CFSFDP) with Polarimetric Decomposition Electronics unmanned aerial vehicle clustering methods man-made targets synthetic aperture radar (SAR) inverse synthetic aperture radar (ISAR) polarimetric decomposition |
| title | Unmanned Aerial Vehicle Recognition Based on Clustering by Fast Search and Find of Density Peaks (CFSFDP) with Polarimetric Decomposition |
| title_full | Unmanned Aerial Vehicle Recognition Based on Clustering by Fast Search and Find of Density Peaks (CFSFDP) with Polarimetric Decomposition |
| title_fullStr | Unmanned Aerial Vehicle Recognition Based on Clustering by Fast Search and Find of Density Peaks (CFSFDP) with Polarimetric Decomposition |
| title_full_unstemmed | Unmanned Aerial Vehicle Recognition Based on Clustering by Fast Search and Find of Density Peaks (CFSFDP) with Polarimetric Decomposition |
| title_short | Unmanned Aerial Vehicle Recognition Based on Clustering by Fast Search and Find of Density Peaks (CFSFDP) with Polarimetric Decomposition |
| title_sort | unmanned aerial vehicle recognition based on clustering by fast search and find of density peaks cfsfdp with polarimetric decomposition |
| topic | unmanned aerial vehicle clustering methods man-made targets synthetic aperture radar (SAR) inverse synthetic aperture radar (ISAR) polarimetric decomposition |
| url | https://www.mdpi.com/2079-9292/7/12/364 |
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