Accession Number:

ADA037678

Title:

Levinson- and Chandrasekhar-Type Equations for a General, Discrete-Time Linear Estimation Problem,

Descriptive Note:

Corporate Author:

STANFORD UNIV CALIF DEPT OF ELECTRICAL ENGINEERING

Report Date:

1976-12-01

Pagination or Media Count:

13.0

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

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE