Optimal measurement scheduling for prediction and estimation

Author

Avitzour, Daniel ; Rogers, Steven R.

Author_Institution

ELTA Electron. Ind., Ashdod, Israel

Volume

38

Issue

10

fYear

1990

fDate

10/1/1990 12:00:00 AM

Firstpage

1733

Lastpage

1739

Abstract

A general theory of optimal measurement scheduling for least-squares estimation is developed. The theory is based on the assumption that the cost of a measurement is inversely proportional to the variance of measurement noise, and that it is possible to distribute the total measurement cost arbitrarily among a set of measurements. The theory leads to a nonquadratic minimization problem. An effective algorithm for solving this problem is developed. The theory is applied to the prediction of a discrete-time integrated Wiener process from noisy past samples

Keywords

filtering and prediction theory; least squares approximations; minimisation; scheduling; signal processing; discrete-time integrated Wiener process; least-squares estimation; measurement cost; nonquadratic minimization problem; optimal measurement scheduling; signal processing; Additive noise; Costs; Energy measurement; Kalman filters; Least squares approximation; Noise measurement; Power measurement; Random variables; Signal processing; Signal to noise ratio;

fLanguage

English

Journal_Title

Acoustics, Speech and Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

0096-3518

Type

jour

DOI

10.1109/29.60104

Filename

60104