Optimal solution of the two-stage Kalman estimator

Chien Shu Hsieh; Fu-Chuang Chen

doi:10.1109/CDC.1995.480355

Optimal solution of the two-stage Kalman estimator

Chien Shu Hsieh^*, Fu-Chuang Chen

^*此作品的通信作者

國立陽明交通大學

研究成果: Conference article › 同行評審

12 引文斯高帕斯（Scopus）

摘要

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.

原文	English
頁（從 - 到）	1532-1537
頁數	6
期刊	Proceedings of the IEEE Conference on Decision and Control
卷	2
DOIs	https://doi.org/10.1109/CDC.1995.480355
出版狀態	Published - 1 12月 1995
事件	Proceedings of the 1995 34th IEEE Conference on Decision and Control. Part 1 (of 4) - New Orleans, LA, USA 持續時間: 13 12月 1995 → 15 12月 1995

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10.1109/CDC.1995.480355

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title = "Optimal solution of the two-stage Kalman estimator",

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.",

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T1 - Optimal solution of the two-stage Kalman estimator

AU - Hsieh, Chien Shu

AU - Chen, Fu-Chuang

PY - 1995/12/1

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N2 - 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.

AB - 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.

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U2 - 10.1109/CDC.1995.480355

DO - 10.1109/CDC.1995.480355

M3 - Conference article

AN - SCOPUS:0029533169

SN - 0191-2216

VL - 2

SP - 1532

EP - 1537

JO - Proceedings of the IEEE Conference on Decision and Control

JF - Proceedings of the IEEE Conference on Decision and Control

T2 - Proceedings of the 1995 34th IEEE Conference on Decision and Control. Part 1 (of 4)

Y2 - 13 December 1995 through 15 December 1995

ER -

Optimal solution of the two-stage Kalman estimator

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