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Levinson and Chandrasekhar-Type Equations for a General Discrete-Time Linear Estimation Problem
Stanford University, CA, USA.
Stanford University, CA, USA.
Stanford University, CA, USA.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
1976 (English)In: Proceedings of the 1976 IEEE Conference on Decision and Control including the 15th Symposium on Adaptive Processes, 1976, 910-915 p.Conference paper, Published paper (Refereed)
Abstract [en]

Recursive algorithms for the solution of linear least-squares estimation problems have been based mainly on state-space models. It has been know, however, that such algorithms exist for stationary time-series, using input-output descriptions (e.g., covariance matrices). We introduce a way of classifying stochastic processes in terms of their "distance" from stationarity that leads to a derivation of an efficient Levinson-type algorithm for arbitrary (nonstationary) processes. By adding structure to the covariance matrix, these general results specialize to state-space type estimation algorithms. In particular, the Chandrasekhar equations are shown to be the natural descendants of the Levinson algorithm.

Place, publisher, year, edition, pages
1976. 910-915 p.
Keyword [en]
Covariance matrix, State-space, Levinson algorithm, Chandrasekhar equation
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-100846DOI: 10.1109/CDC.1976.267856OAI: oai:DiVA.org:liu-100846DiVA: diva2:664021
Conference
1976 IEEE Conference on Decision and Control including the 15th Symposium on Adaptive Processes
Available from: 2013-11-13 Created: 2013-11-13 Last updated: 2013-11-13

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Ljung, Lennart

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