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3D positioning algorithm based on multiple quasi-monostatic IR-UWB radar sensors

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dc.contributor.authorChoi, Jeong Woo-
dc.contributor.authorCho, Sung Ho-
dc.date.accessioned2021-07-30T05:18:34Z-
dc.date.available2021-07-30T05:18:34Z-
dc.date.created2021-05-13-
dc.date.issued2017-06-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/4175-
dc.description.abstractIn this paper, we propose a 3D positioning algorithm based on multiple quasi-monostatic impulse radio ultra-wideband (IR-UWB) radar sensors. Unlike the conventional algorithms such as approximate maximum likelihood (AML) or two stage maximum likelihood (TSML) that find maximum likelihood (ML) positioning solutions using mathematical approximations, the proposed algorithm basically pursues a maximum likelihood (ML) solution based on iterative algorithm using spatial division. The proposed algorithm can be applied to a quasi-monostatic structure in which Tx and Rx are separated, and can be applied to a situation where the distribution of the distance error of each radar is not the same. In addition, the boundary of the target error is used as an important parameter of the algorithm, so that the calculation complexity of the algorithm is fixed. This has the advantage that it can be set appropriately for applications that do not require high accuracy, or for limited computation resource environments such as embedded environments. To verify the performance of the proposed algorithm, we performed simulations.-
dc.language영어-
dc.language.isoen-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.title3D positioning algorithm based on multiple quasi-monostatic IR-UWB radar sensors-
dc.typeArticle-
dc.contributor.affiliatedAuthorCho, Sung Ho-
dc.identifier.doi10.1109/RADAR.2017.7944450-
dc.identifier.scopusid2-s2.0-85021392257-
dc.identifier.bibliographicCitation2017 IEEE Radar Conference, RadarConf 2017, pp.1531 - 1535-
dc.relation.isPartOf2017 IEEE Radar Conference, RadarConf 2017-
dc.citation.title2017 IEEE Radar Conference, RadarConf 2017-
dc.citation.startPage1531-
dc.citation.endPage1535-
dc.type.rimsART-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusApproximation algorithms-
dc.subject.keywordPlusComputational complexity-
dc.subject.keywordPlusIterative methods-
dc.subject.keywordPlusMaximum likelihood-
dc.subject.keywordPlusRadar-
dc.subject.keywordPlusRadar equipment-
dc.subject.keywordPlusRadio communication-
dc.subject.keywordPlus3D positioning-
dc.subject.keywordPlusApproximate maximum likelihood (AML)-
dc.subject.keywordPlusMonostatic radar-
dc.subject.keywordPlusPositioning-
dc.subject.keywordPlusUWB radars-
dc.subject.keywordPlusUltra-wideband (UWB)-
dc.subject.keywordAuthor3D positioning-
dc.subject.keywordAuthorApproximate maximum likelihood (AML)-
dc.subject.keywordAuthorIR-UWB radar-
dc.subject.keywordAuthorMaximum likelihood (ML)-
dc.subject.keywordAuthorPositioning-
dc.subject.keywordAuthorQuasi-monostatic radar-
dc.identifier.urlhttps://ieeexplore.ieee.org/document/7944450-
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