Title :
State and information space estimation: a comparison
Author :
Mutambara, Arthur G O ; Al-Haik, Marwan S Y
Author_Institution :
Dept. of Mech. Eng., FAMU-FSU, Tallahassee, FL, USA
Abstract :
State and information space estimation methods used in both linear and nonlinear systems are compared. The (linear) information filter is introduced as an algebraic equivalent to the Kalman filter. Linear information space is extended to nonlinear information space by outlining the extended information filter. The algebraic equivalence of this filter to the extended Kalman filter and the benefits of nonlinear information space are illustrated by considering a system involving both nonlinear state evolution and nonlinear observations
Keywords :
Kalman filters; nonlinear filters; nonlinear systems; observers; sensor fusion; state-space methods; algebraic equivalence; extended Kalman filter; extended information filter; information space estimation; linear information filter; linear information space; nonlinear information space; nonlinear observations; nonlinear state evolution; state space estimation; Information filtering; Information filters; Integrated circuit modeling; Integrated circuit noise; Linear systems; Nonlinear equations; Nonlinear systems; Recursive estimation; State estimation; Vectors;
Conference_Titel :
American Control Conference, 1997. Proceedings of the 1997
Conference_Location :
Albuquerque, NM
Print_ISBN :
0-7803-3832-4
DOI :
10.1109/ACC.1997.609109
