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New design of linear discrete-time predictor using covariance information

✍ Scribed by Seiichi Nakamori

Publisher
Elsevier Science
Year
1983
Tongue
English
Weight
225 KB
Volume
19
Category
Article
ISSN
0005-1098

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✦ Synopsis

A discrete one-stage predictor algorithm using covariance information in linear systems is derived. The algorithm is obtained for white Gaussian observation noise. The signal is a nonstationary or stationary stochastic process. The autocovariance function of the signal is expressed using a semidegenerate kernel of discrete-time systems. The semi-degenerate kernel can represent general covariance functions of random processes by a finite sum of nonrandom functions.

πŸ“œ SIMILAR VOLUMES

New design of linear least-squares fixed
New design of linear least-squares fixed-interval smoother using covariance information
✍ Seiichi Nakamori; Akira Hataji πŸ“‚ Article πŸ“… 1981 πŸ› Elsevier Science 🌐 English βš– 256 KB

This paper derives recursive linear least-squares fixed-interval smoothing algorithm using covariance information by applying an invariant imbedding method to a Wiener-Hopf integral equation. The algorithm is obtained for the white plus coloured observation noise. The signal process is nonstationary