A Tool for Kalman Filter Tuning

Bernt Magnus Åkesson; John Bagterp Jørgensen; Niels Kjølstad Poulsen; Sten Bay Jørgensen

doi:10.1016/S1570-7946(07)80166-0

A Tool for Kalman Filter Tuning

Bernt Magnus Åkesson
, John Bagterp Jørgensen
, Niels Kjølstad Poulsen
, Sten Bay Jørgensen

Department of Chemical and Biochemical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research

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Abstract

The Kalman filter requires knowledge about the noise statistics. In practical applications, however, the noise covariances are generally not known. A method for estimating noise covariances from process data has been investigated. The method gives a least-squares estimate of the noise covariances, which can be used to compute the Kalman filter gain.

Original language	English
Title of host publication	17th European Symposium on Computer Aided Process Engineering - ESCAPE17, 2007
Number of pages	1392
Volume	Volume 24
Publisher	Elsevier
Publication date	2007
Pages	859-864
ISBN (Print)	978-0-444-53157-5
DOIs	https://doi.org/10.1016/S1570-7946(07)80166-0
Publication status	Published - 2007
Event	17th European Symposium on Computer Aided Process Engineering - Bucharest, Romania Duration: 27 May 2007 → 30 May 2007 Conference number: 17 http://www.escape17.upb.ro/index.htm

Conference

Conference	17th European Symposium on Computer Aided Process Engineering
Number	17
Country/Territory	Romania
City	Bucharest
Period	27/05/2007 → 30/05/2007
Sponsor	University of Bucharest, Technical University of Cluj-Napoca
Internet address	http://www.escape17.upb.ro/index.htm

Series	Computer Aided Chemical Engineering
ISSN	1570-7946

Keywords

Covariance Estimation
State estimation
Kalman filter

Access to Document

10.1016/S1570-7946(07)80166-0

Imm5078Final published version, 124 KB

http://www.elsevierdirect.com/product.jsp?isbn=9780444531575&dmnum=CWS1

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@inproceedings{e660a389855645a0ab3f7d98ebdeb71d,

title = "A Tool for Kalman Filter Tuning",

abstract = "The Kalman filter requires knowledge about the noise statistics. In practical applications, however, the noise covariances are generally not known. A method for estimating noise covariances from process data has been investigated. The method gives a least-squares estimate of the noise covariances, which can be used to compute the Kalman filter gain.",

keywords = "Covariance Estimation, State estimation, Kalman filter",

author = "{\AA}kesson, \{Bernt Magnus\} and J{\o}rgensen, \{John Bagterp\} and Poulsen, \{Niels Kj{\o}lstad\} and J{\o}rgensen, \{Sten Bay\}",

year = "2007",

doi = "10.1016/S1570-7946(07)80166-0",

language = "English",

isbn = "978-0-444-53157-5",

volume = "Volume 24",

series = "Computer Aided Chemical Engineering",

publisher = "Elsevier",

pages = "859--864",

booktitle = "17th European Symposium on Computer Aided Process Engineering - ESCAPE17, 2007",

address = "United Kingdom",

note = "17th European Symposium on Computer Aided Process Engineering, ESCAPE-17 ; Conference date: 27-05-2007 Through 30-05-2007",

url = "http://www.escape17.upb.ro/index.htm",

}

Åkesson, BM, Jørgensen, JB, Poulsen, NK & Jørgensen, SB 2007, A Tool for Kalman Filter Tuning. in 17th European Symposium on Computer Aided Process Engineering - ESCAPE17, 2007. vol. Volume 24, Elsevier, Computer Aided Chemical Engineering, pp. 859-864, 17th European Symposium on Computer Aided Process Engineering, Bucharest, Romania, 27/05/2007. https://doi.org/10.1016/S1570-7946(07)80166-0

A Tool for Kalman Filter Tuning. / Åkesson, Bernt Magnus; Jørgensen, John Bagterp; Poulsen, Niels Kjølstad et al.
17th European Symposium on Computer Aided Process Engineering - ESCAPE17, 2007. Vol. Volume 24 Elsevier, 2007. p. 859-864 (Computer Aided Chemical Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research

TY - GEN

T1 - A Tool for Kalman Filter Tuning

AU - Åkesson, Bernt Magnus

AU - Jørgensen, John Bagterp

AU - Poulsen, Niels Kjølstad

AU - Jørgensen, Sten Bay

N1 - Conference code: 17

PY - 2007

Y1 - 2007

N2 - The Kalman filter requires knowledge about the noise statistics. In practical applications, however, the noise covariances are generally not known. A method for estimating noise covariances from process data has been investigated. The method gives a least-squares estimate of the noise covariances, which can be used to compute the Kalman filter gain.

AB - The Kalman filter requires knowledge about the noise statistics. In practical applications, however, the noise covariances are generally not known. A method for estimating noise covariances from process data has been investigated. The method gives a least-squares estimate of the noise covariances, which can be used to compute the Kalman filter gain.

KW - Covariance Estimation

KW - State estimation

KW - Kalman filter

U2 - 10.1016/S1570-7946(07)80166-0

DO - 10.1016/S1570-7946(07)80166-0

M3 - Article in proceedings

SN - 978-0-444-53157-5

VL - Volume 24

T3 - Computer Aided Chemical Engineering

SP - 859

EP - 864

BT - 17th European Symposium on Computer Aided Process Engineering - ESCAPE17, 2007

PB - Elsevier

T2 - 17th European Symposium on Computer Aided Process Engineering

Y2 - 27 May 2007 through 30 May 2007

ER -

A Tool for Kalman Filter Tuning

Abstract

Conference

Keywords

Access to Document

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