Statements (34)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
nonlinear filter
state estimation algorithm |
| gptkbp:advantage |
no need for explicit Jacobian calculation
better accuracy for highly nonlinear systems |
| gptkbp:application |
gptkb:signal_processing
aerospace autonomous vehicles biomedical engineering finance |
| gptkbp:basedOn |
Unscented Transform
|
| gptkbp:contrastsWith |
gptkb:Extended_Kalman_Filter
|
| gptkbp:developedBy |
gptkb:Jeffrey_K._Uhlmann
gptkb:Simon_J._Julier |
| https://www.w3.org/2000/01/rdf-schema#label |
Unscented Kalman Filter
|
| gptkbp:input |
nonlinear measurement model
nonlinear process model |
| gptkbp:introducedIn |
1997
|
| gptkbp:output |
error covariance
state estimate |
| gptkbp:relatedTo |
gptkb:Kalman_Filter
gptkb:Ensemble_Kalman_Filter Particle Filter |
| gptkbp:supportsAlgorithm |
Bayesian filter
recursive |
| gptkbp:usedFor |
gptkb:navigation
control systems robotics nonlinear state estimation sensor fusion |
| gptkbp:uses |
sigma points
weighted mean and covariance |
| gptkbp:bfsParent |
gptkb:Extended_Kalman_Filter
gptkb:Kalman_Filter |
| gptkbp:bfsLayer |
8
|