Statements (53)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:algorithm
state estimation method |
| gptkbp:alternativeTo |
gptkb:Unscented_Kalman_Filter
Particle Filter |
| gptkbp:appliesTo |
gptkb:navigation
gptkb:signal_processing control systems robotics |
| gptkbp:assumes |
Gaussian noise
Markov chain differentiable models |
| gptkbp:basedOn |
gptkb:Kalman_Filter
|
| gptkbp:category |
recursive filter
Bayesian filter |
| gptkbp:developedBy |
1960s
|
| gptkbp:field |
gptkb:navigation
gptkb:signal_processing control theory robotics |
| gptkbp:form |
gptkb:probability_theory
partial differential equations linear algebra matrix calculus |
| https://www.w3.org/2000/01/rdf-schema#label |
Extended Kalman Filter
|
| gptkbp:input |
nonlinear measurement model
nonlinear process model |
| gptkbp:limitation |
approximation errors
divergence in highly nonlinear systems |
| gptkbp:output |
error covariance
state estimate |
| gptkbp:relatedTo |
gptkb:Kalman_Filter
gptkb:Ensemble_Kalman_Filter gptkb:Unscented_Kalman_Filter Particle Filter |
| gptkbp:requires |
Jacobian matrix
|
| gptkbp:step |
prediction
update linearization covariance update state correction |
| gptkbp:usedFor |
gptkb:SLAM
autonomous vehicles attitude estimation sensor fusion target tracking econometrics aerospace applications mobile robotics GPS navigation |
| gptkbp:usedIn |
nonlinear systems
|
| gptkbp:uses |
linearization
|
| gptkbp:bfsParent |
gptkb:EKF-SLAM
|
| gptkbp:bfsLayer |
7
|