Sammenlign metoder

	Lineær Kvadratisk Gaussisk	Kalman-filter
Fagområde	Reguleringsteknik	Bayesiansk
Familie	Machine learning	Bayesian methods
Oprindelsesår	1960	1960
Ophavsperson	Rudolf Kalman	Rudolf E. Kalman
Type	algorithm	recursive Bayesian filter
Oprindelig kilde	Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.	Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.
Aliasser	LQG, LQR with Kalman Filter	linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter
Relaterede	3	5
Resumé	The Linear Quadratic Gaussian (LQG) controller combines the Linear Quadratic Regulator (LQR) with a Kalman Filter to handle stochastic systems with measurement noise and process noise. Developed by Kalman and later formalized by Athans and others, LQG is the natural stochastic extension of LQR and remains the gold standard for optimal linear control under noise, with applications spanning spacecraft, aircraft autopilot, and industrial process control.	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.