DocumentCode :
1971281
Title :
A finite-difference approach to linearization in nonlinear estimation algorithms
Author :
Schei, Tor Steinar
Author_Institution :
Autom. Control, SINTEF, Trondheim, Norway
Volume :
1
fYear :
1995
fDate :
21-23 Jun 1995
Firstpage :
114
Abstract :
Linearizations of nonlinear functions that are based on Jacobian matrices can often not be applied in practical applications of nonlinear estimation techniques. An alternative linearization method is presented in this paper. The method assumes that covariance matrices are determined on a square root factored form. A factorization of the output covariance from a nonlinear vector function is directly determined by “perturbing” the nonlinear function with the columns of the factored input covariance, without explicitly calculating the linearization and with no differentiations involved. This method seems to be superior to the ordinary Jacobian linearization. It is also an advantage that Jacobian matrices do not have to be derived symbolically
Keywords :
covariance matrices; finite difference methods; linearisation techniques; parameter estimation; state estimation; covariance matrices; factored input covariance; factorization; finite-difference approach; linearization; nonlinear estimation; nonlinear functions; nonlinear vector function; output covariance; square root factored form; Automatic control; Covariance matrix; Finite difference methods; Jacobian matrices; Linear approximation; Noise measurement; Predictive models; Stochastic processes; Taylor series; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference, Proceedings of the 1995
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-2445-5
Type :
conf
DOI :
10.1109/ACC.1995.529219
Filename :
529219
Link To Document :
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