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dc.contributor.authorEinicke, Garry
dc.date.accessioned2018-06-26T03:26:17Z
dc.date.available2018-06-26T03:26:17Z
dc.date.issued2017
dc.identifier.issn0018-9251
dc.identifier.doi10.1109/TAES.2017.2671658
dc.identifier.urihttp://hdl.handle.net/10072/377521
dc.description.abstractA high-order signal model is proposed in which the states are Kronecker tensor products of probability distributions. This model enables an optimal linear filter to be specified. A minimum residual error variance criterion may be used to select the number of discretizations and Kronecker products. The filtering of LIDAR data from a coal shiploader environment is investigated. It is demonstrated that the proposed method can outperform conventional Kalman and hidden Markov model filters.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofpagefrom1481
dc.relation.ispartofpageto1488
dc.relation.ispartofissue3
dc.relation.ispartofjournalIEEE Transactions on Aerospace and Electronic Systems
dc.relation.ispartofvolume53
dc.subject.fieldofresearchInterdisciplinary Engineering not elsewhere classified
dc.subject.fieldofresearchAerospace Engineering
dc.subject.fieldofresearchElectrical and Electronic Engineering
dc.subject.fieldofresearchGeomatic Engineering
dc.subject.fieldofresearchcode091599
dc.subject.fieldofresearchcode0901
dc.subject.fieldofresearchcode0906
dc.subject.fieldofresearchcode0909
dc.titleHigh-Order Filtering of LIDAR Data to Assist Coal Shiploading
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, School of Engineering and Built Environment
gro.hasfulltextNo Full Text
gro.griffith.authorEinicke, Garry


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