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Uncertain Timestamps in Linear State Estimation
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6698-0932
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-1971-4295
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-3270-171X
2019 (English)In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 55, no 3, p. 1334-1346Article in journal (Refereed) Published
Abstract [en]

We consider a linear state estimation problem, where, in addition to the usual timestamped measurements, observations with uncertain timestamps are available. Such observations could, e.g., come from traces left by a target in a tracking scenario or from witnesses of an event and have the potential to improve the estimation accuracy significantly. We derive the posterior distribution and point estimators for a linear Gaussian smoothing formulation of this problem and illustrate with two numerical examples.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. Vol. 55, no 3, p. 1334-1346
Keywords [en]
Linear state estimation, smoothing, tracking, uncertain timestamps
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-157642DOI: 10.1109/TAES.2018.2869648ISI: 000471117500020OAI: oai:DiVA.org:liu-157642DiVA, id: diva2:1326282
Projects
LINK-SICScalable Kalman Filters
Funder
VinnovaSwedish Research CouncilSecurity Link
Note

Funding agencies: Vinnova Industry Excellence Center LINK-SIC; Swedish Strategic Research Center Security Link; Swedish Research Council through the project Scalable Kalman Filters

Available from: 2019-06-17 Created: 2019-06-17 Last updated: 2019-07-15Bibliographically approved

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