Reduced Order Kalman Filtering without Model Reduction

Daniel J. Simon

Reduced Order Kalman Filtering without Model Reduction

Research output: Contribution to journal › Article › peer-review

Abstract

This paper presents all optimal discrete time reduced order Kalman filter. The reduced order filter is used to estimate a linear combination of a subset of the state vector. Most previous approaches to reduced order filtering rely on a reduction of the model order. However, this paper takes the full model order into account. The reduced order filter is obtained by minimizing the trace of the estimation error covariance.

Original language	American English
Journal	Control Intelligent Systems
Volume	35
State	Published - Jan 1 2007

Keywords

Kalman filter
state estimation
order reduction

Disciplines

Electrical and Computer Engineering

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title = "Reduced Order Kalman Filtering without Model Reduction",

abstract = " This paper presents all optimal discrete time reduced order Kalman filter. The reduced order filter is used to estimate a linear combination of a subset of the state vector. Most previous approaches to reduced order filtering rely on a reduction of the model order. However, this paper takes the full model order into account. The reduced order filter is obtained by minimizing the trace of the estimation error covariance.",

keywords = "Kalman filter, state estimation, order reduction",

author = "Simon, \{Daniel J.\}",

note = "Simon, D. D. (2007). Reduced Order Kalman Filtering without Model Reduction. Control Intelligent Systems, 35(2), 169-174.",

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PY - 2007/1/1

Y1 - 2007/1/1

N2 - This paper presents all optimal discrete time reduced order Kalman filter. The reduced order filter is used to estimate a linear combination of a subset of the state vector. Most previous approaches to reduced order filtering rely on a reduction of the model order. However, this paper takes the full model order into account. The reduced order filter is obtained by minimizing the trace of the estimation error covariance.

AB - This paper presents all optimal discrete time reduced order Kalman filter. The reduced order filter is used to estimate a linear combination of a subset of the state vector. Most previous approaches to reduced order filtering rely on a reduction of the model order. However, this paper takes the full model order into account. The reduced order filter is obtained by minimizing the trace of the estimation error covariance.

KW - Kalman filter

KW - state estimation

KW - order reduction

UR - https://engagedscholarship.csuohio.edu/enece_facpub/147

UR - http://proxy.ulib.csuohio.edu:2050/login?url=http://search.ebscohost.com/login.aspx?direct=truedb=iihAN=25605810site=ehost-live

M3 - Article

VL - 35

JO - Control Intelligent Systems

JF - Control Intelligent Systems

ER -

Reduced Order Kalman Filtering without Model Reduction

Abstract

Keywords

Disciplines

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