unscented Kalman filter

E719011

Bayesian filter nonlinear state estimation algorithm recursive estimator

The unscented Kalman filter is a nonlinear state estimation algorithm that uses a deterministic sampling approach (sigma points) to more accurately capture the mean and covariance of a system than the standard extended Kalman filter.

Try in SPARQL Jump to: Statements Referenced by

Statements (49)

Predicate	Object
instanceOf	Bayesian filter ⓘ nonlinear state estimation algorithm ⓘ recursive estimator ⓘ
advantage	higher-order accuracy for nonlinear transformations of Gaussian variables ⓘ no need for linearization ⓘ numerical robustness compared to Jacobian-based methods ⓘ
aimsTo	more accurately capture mean and covariance than extended Kalman filter ⓘ
approximates	posterior covariance ⓘ posterior mean ⓘ
assumes	Gaussian noise ⓘ Gaussian state distribution ⓘ
basedOn	unscented transform ⓘ
comparedTo	extended Kalman filter NERFINISHED ⓘ
coreStep	measurement update ⓘ sigma point generation ⓘ time update ⓘ
doesNotRequire	explicit Jacobian computation ⓘ
estimates	state covariance ⓘ state mean ⓘ state of a dynamic system ⓘ
field	control theory ⓘ estimation theory ⓘ signal processing ⓘ
handles	nonlinear measurement models ⓘ nonlinear process models ⓘ
hasVariant	central difference Kalman filter NERFINISHED ⓘ scaled unscented Kalman filter NERFINISHED ⓘ square-root unscented Kalman filter ⓘ
introducedBy	Jeffrey K. Uhlmann NERFINISHED ⓘ Simon J. Julier NERFINISHED ⓘ
introducedIn	1990s ⓘ
limitation	assumes approximate Gaussianity of distributions ⓘ computational cost grows with state dimension ⓘ
parameter	alpha ⓘ beta ⓘ kappa ⓘ
propagates	sigma points through nonlinear functions ⓘ
relatedTo	Kalman filter NERFINISHED ⓘ extended Kalman filter NERFINISHED ⓘ particle filter ⓘ
usedIn	aerospace guidance and control ⓘ attitude estimation ⓘ autonomous vehicles ⓘ navigation ⓘ robotics ⓘ sensor fusion ⓘ target tracking ⓘ
uses	deterministic sampling ⓘ sigma points ⓘ

Referenced by (1)

Full triples — surface form annotated when it differs from this entity's canonical label.

Kalman filter → hasVariant → unscented Kalman filter ⓘ