Partitioned estimators based on the perturbed kalman filter equations

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

In this paper, the generalized partitioned filter, predictor and smoother formulae for continuous time linear systems in which the partitioned initial states are mutually correlated are derived by using the perturbed Kalman filter equations. It is shown that the results obtained here are extensions of recent results (Lainiotis 1971, Ljung and Kailath 1977) to more general cases, and that the works of Lainiotis and Andrisani II (1979) can be approached without using the partition theorem based on the Bayes estimation theory. Finally, the bias correcting estimators are briefly discussed in order to show the applicability of the formulae.

Original languageEnglish
Pages (from-to)1115-1128
Number of pages14
JournalInternational Journal of Systems Science
Volume14
Issue number9
DOIs
Publication statusPublished - 1983
Externally publishedYes

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Kalman filters
Kalman Filter
Linear systems
Bayes Estimation
Estimator
Estimation Theory
Continuous-time Systems
Predictors
Linear Systems
Partition
Filter
Theorem
Continuous time
Kalman filter

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Management Science and Operations Research

Cite this

Partitioned estimators based on the perturbed kalman filter equations. / Watanabe, Keigo.

In: International Journal of Systems Science, Vol. 14, No. 9, 1983, p. 1115-1128.

Research output: Contribution to journalArticle

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