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dc.contributor.authorPapi, Francesco
dc.contributor.authorBocquel, M.
dc.contributor.authorPodt, M.
dc.contributor.authorBoers, Y.
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.

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.titleIEEE Transactions on Signal Processing
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available

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