|
gptkbp:instanceOf
|
gptkb:algorithm
|
|
gptkbp:alternativeName
|
linear quadratic estimation
|
|
gptkbp:application
|
gptkb:navigation
gptkb:signal_processing
economics
guidance
robotics
time series analysis
|
|
gptkbp:assumes
|
Gaussian noise
linear system dynamics
|
|
gptkbp:category
|
gptkb:stochastic_process
Bayesian estimation
data fusion
time series analysis
|
|
gptkbp:estimatedCost
|
state of a dynamic system
|
|
gptkbp:field
|
gptkb:estimation_theory
gptkb:signal_processing
control theory
|
|
gptkbp:form
|
state-space model
|
|
gptkbp:generalizes
|
gptkb:Ensemble_Kalman_filter
gptkb:Extended_Kalman_filter
gptkb:Particle_filter
gptkb:Square-root_filter
gptkb:Unscented_Kalman_filter
Information filter
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Kalman filter
|
|
gptkbp:introducedIn
|
1960
|
|
gptkbp:inventedBy
|
gptkb:Rudolf_E._Kálmán
|
|
gptkbp:limitation
|
assumes Gaussian noise
assumes linearity
|
|
gptkbp:namedAfter
|
gptkb:Rudolf_E._Kálmán
|
|
gptkbp:openSource
|
gptkb:C++_Eigen_library
gptkb:MATLAB_Kalman_filter_toolbox
gptkb:Python_filterpy
|
|
gptkbp:predictionStep
|
time update
|
|
gptkbp:reduces
|
mean squared error
|
|
gptkbp:relatedTo
|
gptkb:Extended_Kalman_filter
gptkb:Particle_filter
gptkb:Unscented_Kalman_filter
|
|
gptkbp:type
|
linear filter
recursive filter
|
|
gptkbp:updateStep
|
measurement update
|
|
gptkbp:usedBy
|
gptkb:NASA_Apollo_program
autonomous drones
self-driving cars
|
|
gptkbp:usedIn
|
gptkb:GPS
autonomous vehicles
finance
inertial navigation systems
radar tracking
|
|
gptkbp:bfsParent
|
gptkb:Wiener_filter
|
|
gptkbp:bfsLayer
|
3
|
