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Extended Levinson and Chandrasekhar Equations for General Discrete-Time Linear Estimation Problems
Systems Control Incorporated, CA, USA.
Stanford University, CA, USA.
Stanford University, CA, USA.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
1978 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 23, no 4, 653-659 p.Article in journal (Refereed) Published
Abstract [en]

Recursive algorithrms for the solution of linear least-squares estimation problems have been based mainly on state-space models. It has been known, however, that recursive Levinson-Whittle-Wiggins-Robinson (LWR) algorithms exist for stationary time-series, using only input-output information (i.e, covariance matrices). By introducing a way of classifying stochastic processes in terms of an "index of nonstationarity" we derive extended LWR algorithms for nonstationary processes We show also how adding state-space structure to the covariance matrix allows us to specialize these general results to state-space type estimation algorithms. In particular, the Chandrasekhar equations are shown to be natural descendants of the extended LWR algorithm.

Place, publisher, year, edition, pages
IEEE Control Systems Society , 1978. Vol. 23, no 4, 653-659 p.
Keyword [en]
Chandrasekhar equations, Covariance matrices, Least-squares estimation, Linear systems, Recursive estimation
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-100998DOI: 10.1109/TAC.1978.1101797OAI: diva2:664719
Available from: 2013-11-16 Created: 2013-11-16 Last updated: 2013-11-16

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ReferencesLink to record
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