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dc.contributor.authorKhaledian, Homeyra
dc.contributor.authorVilà-Valls, Jordi
dc.contributor.authorChaumette, Eric
dc.contributor.authorPrats Menéndez, Xavier
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Ciència i Tecnologia Aeroespacials
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Física
dc.date.accessioned2021-03-09T08:53:33Z
dc.date.available2021-03-09T08:53:33Z
dc.date.issued2020
dc.identifier.citationKhaledian, H. [et al.]. On Parametric model mismatch in nonlinear EKF Approximations. A: Asilomar Conference on Signals, Systems, and Computers. "Proceeding of 54th Asilomar Conference on Signals, Systems, and Computers". 2020,
dc.identifier.urihttp://hdl.handle.net/2117/341216
dc.description.abstractStandard state estimation techniques, either for linear or nonlinear systems, typically assume a perfect knowledge of the system, i.e., known process and measurement functions, and the corresponding noise statistics. The filter performance strongly depends on such knowledge and a possible model mismatch may lead to a significant performance degradation, but closed-form expressions for the resulting bias and covariance are not available. In this contribution, we explore the impact in extended Kalman filter approximations of a parametric model mismatch on both system functions and provide expressions for the estimator bias and covariance error induced by the mismatch. An illustrative example is shown to support the discussion.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Aeronàutica i espai
dc.subject.lcshKalman filtering
dc.subject.lcshNonlinear systems
dc.subject.lcshPrediction theory
dc.subject.otherNonlinear SSM
dc.subject.otherMMSE estimation
dc.subject.otherEKF
dc.subject.otherModel mismatch
dc.subject.otherSystem uncertainty
dc.subject.otherUncertain inputs
dc.titleOn Parametric model mismatch in nonlinear EKF Approximations
dc.typeConference report
dc.subject.lemacKalman, Filtratge de
dc.subject.lemacSistemes no lineals
dc.subject.lemacPredicció, Teoria de la
dc.contributor.groupUniversitat Politècnica de Catalunya. ICARUS - Intelligent Communications and Avionics for Robust Unmanned Aerial Systems
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
local.identifier.drac29566590
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/783287/EU/The SESAR Knowledge Transfer Network/Engage
local.citation.authorKhaledian, H.; Vilà-Valls, J.; Chaumette, E.; Prats, X.
local.citation.contributorAsilomar Conference on Signals, Systems, and Computers
local.citation.publicationNameProceeding of 54th Asilomar Conference on Signals, Systems, and Computers


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