Estimation of parameters in a linear state space model using a Rao-Blackwellised particle filter

P. Li, R. Goodall, V. Kadirkamanathan

Research output: Contribution to journalArticle

83 Citations (Scopus)

Abstract

A Rao-Blackwellised particle filter is used in the estimation of the parameters of a linear stochastic state space model. The proposed method combines the particle filtering technique with a Kalman filter using marginalisation so as to make full use of the analytically tractable structure of the model. Simulation studies are performed on a simple illustrative example and the results demonstrate the effectiveness of the proposed method in comparison with the conventional extended-Kalman-filler-based method. The proposed method is then applied in the estimation of the parameters in a railway vehicle dynamic model for condition monitoring and the desired results have been obtained.

LanguageEnglish
Pages727-738
Number of pages12
JournalIEE Proceedings: Control Theory and Applications
Volume151
Issue number6
DOIs
Publication statusPublished - 22 Nov 2004
Externally publishedYes

Fingerprint

filters
Condition monitoring
Kalman filters
fillers
dynamic models
Fillers
Dynamic models
vehicles
simulation

Cite this

@article{e3cf5858c282437cabfa3f15c6ca4920,
title = "Estimation of parameters in a linear state space model using a Rao-Blackwellised particle filter",
abstract = "A Rao-Blackwellised particle filter is used in the estimation of the parameters of a linear stochastic state space model. The proposed method combines the particle filtering technique with a Kalman filter using marginalisation so as to make full use of the analytically tractable structure of the model. Simulation studies are performed on a simple illustrative example and the results demonstrate the effectiveness of the proposed method in comparison with the conventional extended-Kalman-filler-based method. The proposed method is then applied in the estimation of the parameters in a railway vehicle dynamic model for condition monitoring and the desired results have been obtained.",
author = "P. Li and R. Goodall and V. Kadirkamanathan",
year = "2004",
month = "11",
day = "22",
doi = "10.1049/ip-cta:20041008",
language = "English",
volume = "151",
pages = "727--738",
journal = "IEE Proceedings: Control Theory and Applications",
issn = "1350-2379",
publisher = "Institute of Electrical Engineers",
number = "6",

}

Estimation of parameters in a linear state space model using a Rao-Blackwellised particle filter. / Li, P.; Goodall, R.; Kadirkamanathan, V.

In: IEE Proceedings: Control Theory and Applications, Vol. 151, No. 6, 22.11.2004, p. 727-738.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Estimation of parameters in a linear state space model using a Rao-Blackwellised particle filter

AU - Li, P.

AU - Goodall, R.

AU - Kadirkamanathan, V.

PY - 2004/11/22

Y1 - 2004/11/22

N2 - A Rao-Blackwellised particle filter is used in the estimation of the parameters of a linear stochastic state space model. The proposed method combines the particle filtering technique with a Kalman filter using marginalisation so as to make full use of the analytically tractable structure of the model. Simulation studies are performed on a simple illustrative example and the results demonstrate the effectiveness of the proposed method in comparison with the conventional extended-Kalman-filler-based method. The proposed method is then applied in the estimation of the parameters in a railway vehicle dynamic model for condition monitoring and the desired results have been obtained.

AB - A Rao-Blackwellised particle filter is used in the estimation of the parameters of a linear stochastic state space model. The proposed method combines the particle filtering technique with a Kalman filter using marginalisation so as to make full use of the analytically tractable structure of the model. Simulation studies are performed on a simple illustrative example and the results demonstrate the effectiveness of the proposed method in comparison with the conventional extended-Kalman-filler-based method. The proposed method is then applied in the estimation of the parameters in a railway vehicle dynamic model for condition monitoring and the desired results have been obtained.

UR - http://www.scopus.com/inward/record.url?scp=10944253539&partnerID=8YFLogxK

U2 - 10.1049/ip-cta:20041008

DO - 10.1049/ip-cta:20041008

M3 - Article

VL - 151

SP - 727

EP - 738

JO - IEE Proceedings: Control Theory and Applications

T2 - IEE Proceedings: Control Theory and Applications

JF - IEE Proceedings: Control Theory and Applications

SN - 1350-2379

IS - 6

ER -