Levinson and Chandrasekhar-Type Equations for a General Discrete-Time Linear Estimation Problem
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 (Refereed)
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.
Covariance matrix, State-space, Levinson algorithm, Chandrasekhar equation
IdentifiersURN: urn:nbn:se:liu:diva-100846DOI: 10.1109/CDC.1976.267856OAI: oai:DiVA.org:liu-100846DiVA: diva2:664021
1976 IEEE Conference on Decision and Control including the 15th Symposium on Adaptive Processes