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IMM EKF based sensor fusion for vehicle positioning under various road surface conditions

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dc.contributor.authorHeo, H.U.-
dc.contributor.authorJung, Kim D.-
dc.contributor.authorChung, C.C.-
dc.date.accessioned2021-08-06T02:45:16Z-
dc.date.available2021-08-06T02:45:16Z-
dc.date.created2021-08-06-
dc.date.issued2020-10-13-
dc.identifier.issn1598-7833-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/84693-
dc.description.abstractIn this paper, we propose an estimation of the accurate vehicle position using Interacting Multiple Model Extended Kalman Filter (IMM EKF) when road surface varies. Since the vehicle has different cornering stiffness as the road surface varies, it is difficult to accurately estimate the position of the vehicle. To resolve this problem, we present the IMM EKF considering each model of different roads to improve the estimation performance. From the numerical simulation using MATLAB/CARSIM, we observed that the performance of the proposed algorithm improves vehicle positioning performance.-
dc.language영어-
dc.language.isoen-
dc.publisherIEEE Computer Society-
dc.titleIMM EKF based sensor fusion for vehicle positioning under various road surface conditions-
dc.typeConference-
dc.contributor.affiliatedAuthorChung, C.C.-
dc.identifier.scopusid2-s2.0-85098052258-
dc.identifier.bibliographicCitation2020 20th International Conference on Control, Automation and Systems (ICCAS), pp.1220 - 1224-
dc.relation.isPartOf2020 20th International Conference on Control, Automation and Systems (ICCAS)-
dc.relation.isPartOf2020 20th International Conference on Control, Automation and Systems (ICCAS)-
dc.citation.title2020 20th International Conference on Control, Automation and Systems (ICCAS)-
dc.citation.startPage1220-
dc.citation.endPage1224-
dc.citation.conferencePlaceKO-
dc.citation.conferencePlaceBusan, Korea-
dc.citation.conferenceDate2020-10-13-
dc.type.rimsCONF-
dc.description.journalClass1-
dc.identifier.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9268405-
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