Show simple item record

dc.contributor.authorPapi, Francesco
dc.contributor.authorBocquel, M.
dc.contributor.authorPodt, M.
dc.contributor.authorBoers, Y.
dc.date.accessioned2017-01-30T13:10:12Z
dc.date.available2017-01-30T13:10:12Z
dc.date.created2015-10-29T04:09:49Z
dc.date.issued2014
dc.identifier.citationPapi, F. and Bocquel, M. and Podt, M. and Boers, Y. 2014. Fixed-lag smoothing for bayes optimal knowledge exploitation in target tracking. IEEE Transactions on Signal Processing. 62 (12): pp. 3143-3152.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/29075
dc.identifier.doi10.1109/TSP.2014.2321731
dc.description.abstract

In this work, we are interested in the improvements attainable when multiscan processing of external knowledge is performed over a moving time window. We propose a novel algorithm that enforces the state constraints by using a Fixed-Lag Smoothing procedure within the prediction step of the Bayesian recursion. For proving the improvements, we utilize differential entropy as a measure of uncertainty and show that the approach guarantees a lower or equal posterior differential entropy than classical single-step constrained filtering. Simulation results using examples for single-target tracking are presented to verify that a Sequential Monte Carlo implementation of the proposed algorithm guarantees an improved tracking accuracy. © 2014 IEEE.

dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.titleFixed-lag smoothing for bayes optimal knowledge exploitation in target tracking
dc.typeJournal Article
dcterms.source.volume62
dcterms.source.number12
dcterms.source.startPage3143
dcterms.source.endPage3152
dcterms.source.issn1053-587X
dcterms.source.titleIEEE Transactions on Signal Processing
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record