Comparer des méthodes

	Filtre de Kalman	Filtre de Kalman Étendu
Domaine	Bayésien	Théorie du contrôle
Famille	Bayesian methods	Machine learning
Année d'origine	1960	1961
Auteur d'origine	Rudolf E. Kalman	Richard S. Bucy
Type	recursive Bayesian filter	algorithm
Source fondatrice	Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.	Bucy, R. S. (1961). A linear approximation to the solution of nonlinear filtering equations. Technical Report No. 32-486, Jet Propulsion Laboratory.
Alias	linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter	EKF, Nonlinear Kalman Filter
Apparentées	5	2
Résumé	The Kalman filter is an optimal recursive algorithm for estimating the hidden state of a linear dynamical system from noisy measurements. At each time step it alternates between a prediction step — projecting the state forward using the system model — and an update step that corrects the prediction with the new observation, producing minimum-variance state estimates and their uncertainty in real time.	The Extended Kalman Filter (EKF) is the nonlinear generalization of the Kalman Filter, extending the linear state estimation algorithm to nonlinear systems through local linearization. Developed by Bucy in the early 1960s, the EKF has become the workhorse for state estimation in nonlinear systems across robotics, aerospace, and navigation, enabling real-time processing of noisy measurements from nonlinear sensors and dynamics.