Optimal minimal-order least-squares estimators via the general two-stage Kalman filter

Chien Shu Hsieh*, Fu-Chuang Chen

*Corresponding author for this work

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

A direct derivation of the optimal minimal-order least-squares estimator (OMOLSE) is presented using the recently developed general two-stage Kalman filter (GTSKF). Using this new result, the reduced-order estimators of O'Reilly and Fairman are recently shown to be equivalent. A practical implementation issue to consider these two estimators is also addressed.

Original languageEnglish
Pages (from-to)1772-1776
Number of pages5
JournalIEEE Transactions on Automatic Control
Volume46
Issue number11
DOIs
StatePublished - 1 Nov 2001

Keywords

  • Least-squares estimator
  • Minimal-order estimator
  • Reduced-order estimator
  • Two-stage Kalman filter

Fingerprint Dive into the research topics of 'Optimal minimal-order least-squares estimators via the general two-stage Kalman filter'. Together they form a unique fingerprint.

  • Cite this