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Particle Learning Methods for State and Parameter Estimation

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Particle Learning Methods for State and Parameter Estimation. / Nemeth, Christopher; Fearnhead, Paul; Mihaylova, Lyudmila et al.
Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET. 2012.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Nemeth, C, Fearnhead, P, Mihaylova, L & Vorley, D 2012, Particle Learning Methods for State and Parameter Estimation. in Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET. The 9th IET Data Fusion & Target Tracking Conference (DF & TT'2012). Algorithms & Applications, United Kingdom, 16/05/12. https://doi.org/10.1049/cp.2012.0412

APA

Nemeth, C., Fearnhead, P., Mihaylova, L., & Vorley, D. (2012). Particle Learning Methods for State and Parameter Estimation. In Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET https://doi.org/10.1049/cp.2012.0412

Vancouver

Nemeth C, Fearnhead P, Mihaylova L, Vorley D. Particle Learning Methods for State and Parameter Estimation. In Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET. 2012 doi: 10.1049/cp.2012.0412

Author

Nemeth, Christopher ; Fearnhead, Paul ; Mihaylova, Lyudmila et al. / Particle Learning Methods for State and Parameter Estimation. Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET. 2012.

Bibtex

@inproceedings{6583bc436163498f9de32e0880284c5d,
title = "Particle Learning Methods for State and Parameter Estimation",
abstract = "This paper presents an approach for online parameter estimation within particle lters. Current research has mainly been focused towards the estimation of static parameters. However, in scenarios of target maneuver-ability, it is often necessary to update the parameters of the model to meet the changing conditions of the target. The novel aspect of the proposed approach lies in the estimation of non-static parameters which change at some unknown point in time. Our parameter estimation is updated using changepoint analysis, where a changepoint is identied when a signicant change occurs in the observations of the system, such as changes in direction or velocity. ",
keywords = "parameter estimation, Monte Carlo methods, nonlinear filtering, changepoint detection",
author = "Christopher Nemeth and Paul Fearnhead and Lyudmila Mihaylova and D. Vorley",
year = "2012",
month = may,
day = "15",
doi = "10.1049/cp.2012.0412",
language = "English",
booktitle = "Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET",
note = "The 9th IET Data Fusion & Target Tracking Conference (DF & TT'2012). Algorithms & Applications ; Conference date: 16-05-2012 Through 17-05-2012",

}

RIS

TY - GEN

T1 - Particle Learning Methods for State and Parameter Estimation

AU - Nemeth, Christopher

AU - Fearnhead, Paul

AU - Mihaylova, Lyudmila

AU - Vorley, D.

PY - 2012/5/15

Y1 - 2012/5/15

N2 - This paper presents an approach for online parameter estimation within particle lters. Current research has mainly been focused towards the estimation of static parameters. However, in scenarios of target maneuver-ability, it is often necessary to update the parameters of the model to meet the changing conditions of the target. The novel aspect of the proposed approach lies in the estimation of non-static parameters which change at some unknown point in time. Our parameter estimation is updated using changepoint analysis, where a changepoint is identied when a signicant change occurs in the observations of the system, such as changes in direction or velocity.

AB - This paper presents an approach for online parameter estimation within particle lters. Current research has mainly been focused towards the estimation of static parameters. However, in scenarios of target maneuver-ability, it is often necessary to update the parameters of the model to meet the changing conditions of the target. The novel aspect of the proposed approach lies in the estimation of non-static parameters which change at some unknown point in time. Our parameter estimation is updated using changepoint analysis, where a changepoint is identied when a signicant change occurs in the observations of the system, such as changes in direction or velocity.

KW - parameter estimation

KW - Monte Carlo methods

KW - nonlinear filtering

KW - changepoint detection

U2 - 10.1049/cp.2012.0412

DO - 10.1049/cp.2012.0412

M3 - Conference contribution/Paper

BT - Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET

T2 - The 9th IET Data Fusion & Target Tracking Conference (DF & TT'2012). Algorithms & Applications

Y2 - 16 May 2012 through 17 May 2012

ER -