DocumentCode :
115747
Title :
An extended Kalman filter with a computed mean square error bound
Author :
Hexner, G. ; Weiss, H.
Author_Institution :
Adv. Defense Syst., RAFAEL, Haifa, Israel
fYear :
2014
fDate :
15-17 Dec. 2014
Firstpage :
5008
Lastpage :
5014
Abstract :
The paper proposes a new recursive filter for non-linear systems that inherently computes a valid bound on the mean square estimation error. The proposed filter, bound based extended Kalman, (BEKF) is in the form of an extended Kalman filter. The main difference of the proposed filter from the conventional extended Kalman filter is in the use of a computed mean square error bound matrix, to calculate the filter gain, and to serve as bound on the actual mean square error. The paper shows that when the system is linear the proposed filtering algorithm reduces to the conventional Kalman filter. The theory presented in the paper is applicable to a wide class of systems, but if the system is polynomial, then the recently developed theory of positive polynomials considerably simplifies the filter´s implementation.
Keywords :
Kalman filters; matrix algebra; mean square error methods; nonlinear control systems; polynomials; recursive filters; BEKF; extended Kalman filter; filtering algorithm; mean square error bound matrix; nonlinear system; polynomials; recursive filter; Covariance matrices; Estimation error; Kalman filters; Mean square error methods; Polynomials; Standards; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on
Conference_Location :
Los Angeles, CA
Print_ISBN :
978-1-4799-7746-8
Type :
conf
DOI :
10.1109/CDC.2014.7040171
Filename :
7040171
Link To Document :
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