Levinson- and Chandrasekhar-Type Equations for a General, Discrete-Time Linear Estimation Problem,
Abstract
Recursive algorithms for the solution of linear least-squares estimation problems have been based mainly on state-space models. It has been known, 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 descondants of the Levinson algorithm. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 1976
- Accession Number
- ADA037678
Entities
People
- B. Friedlander
- L. Ljung
- M. Morf
- Thomas Kailath
Organizations
- Stanford University