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DC Field | Value | Language |
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dc.citation.endPage | 400 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 393 | - |
dc.citation.title | IET RADAR SONAR AND NAVIGATION | - |
dc.citation.volume | 7 | - |
dc.contributor.author | Oh, Hyondong | - |
dc.contributor.author | Shin, Hyo-Sang | - |
dc.contributor.author | Kim, Seungkeun | - |
dc.contributor.author | Tsourdos, Antonios | - |
dc.contributor.author | White, Brian A | - |
dc.date.accessioned | 2023-12-22T04:07:44Z | - |
dc.date.available | 2023-12-22T04:07:44Z | - |
dc.date.created | 2016-08-12 | - |
dc.date.issued | 2013-04 | - |
dc.description.abstract | This study proposes an airborne behaviour monitoring methodology of ground vehicles based on a statistical learning approach with domain knowledge given by road map information. To monitor and track the moving ground target using unmanned aerial vehicle aboard a moving target indicator, an interactive multiple model (IMM) filter is firstly applied. The IMM filter consists of an on-road moving mode using a road-constrained filter and an off-road moving mode using a conventional filter. Mode probability is also calculated from the IMM filter, and it provides deviation of the vehicle from the road. Then, a novel hybrid algorithm for anomalous behaviour recognition is developed using a Gaussian process regression on velocity profile along the one-dimensionalised position of the vehicle, as well as the deviation of the vehicle. To verify the feasibility and benefits of the proposed approach, a numerical simulation is performed using realistic car trajectory data in a city traffic. © The Institution of Engineering and Technology 2013 | - |
dc.identifier.bibliographicCitation | IET RADAR SONAR AND NAVIGATION, v.7, no.4, pp.393 - 400 | - |
dc.identifier.doi | 10.1049/iet-rsn.2012.0255 | - |
dc.identifier.issn | 1751-8784 | - |
dc.identifier.scopusid | 2-s2.0-84881507259 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/20217 | - |
dc.identifier.url | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6573721&tag=1 | - |
dc.identifier.wosid | 000322799100007 | - |
dc.language | 영어 | - |
dc.publisher | INST ENGINEERING TECHNOLOGY-IET | - |
dc.title | Airborne behaviour monitoring using Gaussian processes with map information | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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