Optimal solution of the two-stage Kalman estimator

Chien Shu Hsieh*, Fu-Chuang Chen

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

8 Scopus citations

Abstract

The optimal solution of estimating a set of dynamic state in the presence of a random bias employing a two-stage Kalman estimator is addressed. It is well known that, under an algebraic constraint, the optimal estimate of the system state can be obtained from a two-stage Kalman estimator. Unfortunately, this algebraic constraint is seldom satisfied for practical systems. This paper proposes a general form of the optimal solution of the two-stage estimator, in which the algebraic constraint is removed. Furthermore, it is shown that, by applying the adaptive process noise covariance concept, the optimal solution of the two-stage Kalman estimator is composed of a modified bias-free filter and an bias-compensating filter, which can be viewed as a generalized form of the conventional two-stage Kalman estimator.

Original languageEnglish
Pages (from-to)1532-1537
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
Volume2
DOIs
StatePublished - 1 Dec 1995
EventProceedings of the 1995 34th IEEE Conference on Decision and Control. Part 1 (of 4) - New Orleans, LA, USA
Duration: 13 Dec 199515 Dec 1995

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