Vertaile menetelmiä

	Kalman-suodin	Laajennettu Kalman-suodin (EKF)
Tieteenala	Bayesilainen tilastotiede	Säätöteoria
Menetelmäperhe	Bayesian methods	Machine learning
Syntyvuosi	1960	1961
Kehittäjä	Rudolf E. Kalman	Richard S. Bucy
Tyyppi	recursive Bayesian filter	algorithm
Alkuperäislähde	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.
Rinnakkaisnimet	linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter	EKF, Nonlinear Kalman Filter
Liittyvät	5	2
Tiivistelmä	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.