Computationally efficient Kalman filtering for a class of nonlinear systems

Charalampidis, AC; Papavassilopoulos, GP

dc.contributor.author	Charalampidis, AC	en
dc.contributor.author	Papavassilopoulos, GP	en
dc.date.accessioned	2014-03-01T01:35:27Z
dc.date.available	2014-03-01T01:35:27Z
dc.date.issued	2011	en
dc.identifier.issn	0018-9286	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/21052
dc.subject	Covariance matrices	en
dc.subject	nonlinear filters	en
dc.subject	numerical methods	en
dc.subject	recursive state estimation	en
dc.subject	state space methods	en
dc.subject	unscented Kalman filtering	en
dc.subject.classification	Automation & Control Systems	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Computational costs	en
dc.subject.other	Computationally efficient	en
dc.subject.other	Covariance matrices	en
dc.subject.other	Discrete-time nonlinear systems	en
dc.subject.other	Hermite	en
dc.subject.other	Illustrative examples	en
dc.subject.other	Kalman-filtering	en
dc.subject.other	Non-Linearity	en
dc.subject.other	Nonlinear filter	en
dc.subject.other	Recursive state estimation	en
dc.subject.other	Special structure	en
dc.subject.other	Unscented Kalman Filter	en
dc.subject.other	Unscented Kalman filtering	en
dc.subject.other	Variable functions	en
dc.subject.other	Covariance matrix	en
dc.subject.other	Estimation	en
dc.subject.other	Kalman filters	en
dc.subject.other	Nonlinear analysis	en
dc.subject.other	Nonlinear filtering	en
dc.subject.other	Nonlinear systems	en
dc.subject.other	Recursive functions	en
dc.subject.other	State estimation	en
dc.subject.other	State space methods	en
dc.subject.other	Numerical methods	en
dc.title	Computationally efficient Kalman filtering for a class of nonlinear systems	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TAC.2010.2078090	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TAC.2010.2078090	en
heal.identifier.secondary	5582210	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	This paper deals with recursive state estimation for the class of discrete time nonlinear systems whose nonlinearity consists of one or more static nonlinear one-variable functions. This class contains several important subclasses. The special structure is exploited to permit accurate computations without an increase in computational cost. The proposed method is compared with standard Extended Kalman Filter, Unscented Kalman Filter and Gauss-Hermite Kalman Filter in three illustrative examples. The results show that it yields good results with small computational cost. © 2006 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Automatic Control	en
dc.identifier.doi	10.1109/TAC.2010.2078090	en
dc.identifier.isi	ISI:000289211100001	en
dc.identifier.volume	56	en
dc.identifier.issue	3	en
dc.identifier.spage	483	en
dc.identifier.epage	491	en