State Matrix Kalman Filter

Daniel Choukroun; Itzhack Y. Bar-Itzhack; Haim Weiss; Yaakov Oshman

State Matrix Kalman Filter

Daniel Choukroun, Itzhack Y. Bar-Itzhack, Haim Weiss, Yaakov Oshman

Research output: Contribution to journal › Conference article › peer-review

7 Scopus citations

Abstract

The paper presents a general discrete-time Kalman filter for state matrix estimation using matrix measurements. The new algorithm evaluates the state matrix estimate and the estimation error covariance matrix in terms of the original system matrices. The proposed algorithm naturally fits systems which are most conveniently described by matrix process and measurement equations. Its formulation uses a compact notation for aiding both intuition and mathematical manipulation. It is a straightforward extension of the classical Kalman filter, and includes as special cases other matrix filters that were developed in the past.

Original language	English
Pages (from-to)	393-398
Number of pages	6
Journal	Proceedings of the IEEE Conference on Decision and Control
Volume	1
State	Published - 1 Dec 2003
Externally published	Yes
Event	42nd IEEE Conference on Decision and Control - Maui, HI, United States Duration: 9 Dec 2003 → 12 Dec 2003

ASJC Scopus subject areas

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

Cite this

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abstract = "The paper presents a general discrete-time Kalman filter for state matrix estimation using matrix measurements. The new algorithm evaluates the state matrix estimate and the estimation error covariance matrix in terms of the original system matrices. The proposed algorithm naturally fits systems which are most conveniently described by matrix process and measurement equations. Its formulation uses a compact notation for aiding both intuition and mathematical manipulation. It is a straightforward extension of the classical Kalman filter, and includes as special cases other matrix filters that were developed in the past.",

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AU - Choukroun, Daniel

AU - Bar-Itzhack, Itzhack Y.

AU - Weiss, Haim

AU - Oshman, Yaakov

PY - 2003/12/1

Y1 - 2003/12/1

N2 - The paper presents a general discrete-time Kalman filter for state matrix estimation using matrix measurements. The new algorithm evaluates the state matrix estimate and the estimation error covariance matrix in terms of the original system matrices. The proposed algorithm naturally fits systems which are most conveniently described by matrix process and measurement equations. Its formulation uses a compact notation for aiding both intuition and mathematical manipulation. It is a straightforward extension of the classical Kalman filter, and includes as special cases other matrix filters that were developed in the past.

AB - The paper presents a general discrete-time Kalman filter for state matrix estimation using matrix measurements. The new algorithm evaluates the state matrix estimate and the estimation error covariance matrix in terms of the original system matrices. The proposed algorithm naturally fits systems which are most conveniently described by matrix process and measurement equations. Its formulation uses a compact notation for aiding both intuition and mathematical manipulation. It is a straightforward extension of the classical Kalman filter, and includes as special cases other matrix filters that were developed in the past.

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M3 - Conference article

AN - SCOPUS:1542359117

SN - 0191-2216

VL - 1

SP - 393

EP - 398

JO - Proceedings of the IEEE Conference on Decision and Control

JF - Proceedings of the IEEE Conference on Decision and Control

T2 - 42nd IEEE Conference on Decision and Control

Y2 - 9 December 2003 through 12 December 2003

ER -

State Matrix Kalman Filter

Abstract

ASJC Scopus subject areas

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