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Statements

Subject Item

dbr:Invariant_extended_Kalman_filter

rdf:type

yago:Instrumentality103575240 yago:Artifact100021939 yago:Object100002684 dbo:Work yago:Device103183080 yago:PhysicalEntity100001930 yago:Filter103339643 yago:Whole100003553 yago:WikicatNonlinearFilters

rdfs:label

Invariant extended Kalman filter

rdfs:comment

The invariant extended Kalman filter (IEKF) (not to be confused with the iterated extended Kalman filter) was first introduced as a version of the extended Kalman filter (EKF) for nonlinear systems possessing symmetries (or invariances), then generalized and recast as an adaptation to Lie groups of the linear Kalman filtering theory. Instead of using a linear correction term based on a linear output error, the IEKF uses a geometrically adapted correction term based on an invariant output error; in the same way the gain matrix is not updated from a linear state error, but from an invariant state error. The main benefit is that the gain and covariance equations have reduced dependence on the estimated value of the state. In some cases they converge to constant values on a much bigger set of

dcterms:subject

dbc:Signal_estimation dbc:Nonlinear_filters

dbo:wikiPageID

27461950

dbo:wikiPageRevisionID

1109096672

dbo:wikiPageWikiLink

dbc:Nonlinear_filters dbr:Invariant_(mathematics) dbr:Transformation_group dbr:Exponential_map_(Lie_theory) dbc:Signal_estimation dbr:Lie_group dbr:Quaternion dbr:Simultaneous_localization_and_mapping dbr:Symmetry-preserving_filter dbr:Tangent_space dbr:Kalman_filter dbr:Kalman_filtering dbr:Attitude_and_Heading_Reference_Systems dbr:State_space dbr:Extended_Kalman_filter dbr:White_Gaussian_noise dbr:Inertial_navigation dbr:Group_action dbr:Group_automorphism

owl:sameAs

n10:4nqax wikidata:Q6059511 freebase:m.0b__psz yago-res:Invariant_extended_Kalman_filter

dbo:abstract

The invariant extended Kalman filter (IEKF) (not to be confused with the iterated extended Kalman filter) was first introduced as a version of the extended Kalman filter (EKF) for nonlinear systems possessing symmetries (or invariances), then generalized and recast as an adaptation to Lie groups of the linear Kalman filtering theory. Instead of using a linear correction term based on a linear output error, the IEKF uses a geometrically adapted correction term based on an invariant output error; in the same way the gain matrix is not updated from a linear state error, but from an invariant state error. The main benefit is that the gain and covariance equations have reduced dependence on the estimated value of the state. In some cases they converge to constant values on a much bigger set of trajectories than is the case for the EKF, which results in a better convergence of the estimation.

gold:hypernym

dbr:Version

prov:wasDerivedFrom

wikipedia-en:Invariant_extended_Kalman_filter?oldid=1109096672&ns=0

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12304

foaf:isPrimaryTopicOf

wikipedia-en:Invariant_extended_Kalman_filter