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Group Object Tracking with a Sequential Monte Carlo Method Based on a Parameterised Likelihood Function

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published

Standard

Group Object Tracking with a Sequential Monte Carlo Method Based on a Parameterised Likelihood Function. / Petrov, Nikolay; Mihaylova, Lyudmila; Gning, Amadou et al.
Monte Carlo Methods and Applications : Proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29 - September 2, 2011, Borovets, Bulgaria. ed. / Karl K. Sabelfeld; Ivan Dimov. De Gruyter, 2012. p. 171-180 (De Gruyter Proceedings in Mathematics).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Petrov, N, Mihaylova, L, Gning, A & Angelova, D 2012, Group Object Tracking with a Sequential Monte Carlo Method Based on a Parameterised Likelihood Function. in KK Sabelfeld & I Dimov (eds), Monte Carlo Methods and Applications : Proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29 - September 2, 2011, Borovets, Bulgaria. De Gruyter Proceedings in Mathematics, De Gruyter, pp. 171-180. <http://www.degruyter.com/view/product/184575>

APA

Petrov, N., Mihaylova, L., Gning, A., & Angelova, D. (2012). Group Object Tracking with a Sequential Monte Carlo Method Based on a Parameterised Likelihood Function. In K. K. Sabelfeld, & I. Dimov (Eds.), Monte Carlo Methods and Applications : Proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29 - September 2, 2011, Borovets, Bulgaria (pp. 171-180). (De Gruyter Proceedings in Mathematics). De Gruyter. http://www.degruyter.com/view/product/184575

Vancouver

Petrov N, Mihaylova L, Gning A, Angelova D. Group Object Tracking with a Sequential Monte Carlo Method Based on a Parameterised Likelihood Function. In Sabelfeld KK, Dimov I, editors, Monte Carlo Methods and Applications : Proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29 - September 2, 2011, Borovets, Bulgaria. De Gruyter. 2012. p. 171-180. (De Gruyter Proceedings in Mathematics).

Author

Petrov, Nikolay ; Mihaylova, Lyudmila ; Gning, Amadou et al. / Group Object Tracking with a Sequential Monte Carlo Method Based on a Parameterised Likelihood Function. Monte Carlo Methods and Applications : Proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29 - September 2, 2011, Borovets, Bulgaria. editor / Karl K. Sabelfeld ; Ivan Dimov. De Gruyter, 2012. pp. 171-180 (De Gruyter Proceedings in Mathematics).

Bibtex

@inproceedings{6c59d8985d4b421a853181ecc8458772,
title = "Group Object Tracking with a Sequential Monte Carlo Method Based on a Parameterised Likelihood Function",
abstract = "Group objects are characterised with multiple measurements originating from different locations of the targets constituting the group. This paper presents a novel Sequential Monte Carlo approach for tracking groups with a large number of components, applicable to various nonlinear problems. The novelty in this work is in the derivation of the likelihood function for nonlinear measurement functions, with sets of measurements belonging to a bounded spatial region. Simulation results are presented when a group of 50 objects is surrounded by a circular region. Estimation results are given for the group object center and extent.",
keywords = "sequential Monte Carlo methods, measurement uncertainty, nonlinear estimation, group object tracking",
author = "Nikolay Petrov and Lyudmila Mihaylova and Amadou Gning and Donka Angelova",
year = "2012",
month = dec,
language = "English",
series = "De Gruyter Proceedings in Mathematics",
publisher = "De Gruyter",
pages = "171--180",
editor = "Sabelfeld, {Karl K.} and Dimov, {Ivan }",
booktitle = "Monte Carlo Methods and Applications",

}

RIS

TY - GEN

T1 - Group Object Tracking with a Sequential Monte Carlo Method Based on a Parameterised Likelihood Function

AU - Petrov, Nikolay

AU - Mihaylova, Lyudmila

AU - Gning, Amadou

AU - Angelova, Donka

PY - 2012/12

Y1 - 2012/12

N2 - Group objects are characterised with multiple measurements originating from different locations of the targets constituting the group. This paper presents a novel Sequential Monte Carlo approach for tracking groups with a large number of components, applicable to various nonlinear problems. The novelty in this work is in the derivation of the likelihood function for nonlinear measurement functions, with sets of measurements belonging to a bounded spatial region. Simulation results are presented when a group of 50 objects is surrounded by a circular region. Estimation results are given for the group object center and extent.

AB - Group objects are characterised with multiple measurements originating from different locations of the targets constituting the group. This paper presents a novel Sequential Monte Carlo approach for tracking groups with a large number of components, applicable to various nonlinear problems. The novelty in this work is in the derivation of the likelihood function for nonlinear measurement functions, with sets of measurements belonging to a bounded spatial region. Simulation results are presented when a group of 50 objects is surrounded by a circular region. Estimation results are given for the group object center and extent.

KW - sequential Monte Carlo methods

KW - measurement uncertainty

KW - nonlinear estimation

KW - group object tracking

M3 - Conference contribution/Paper

T3 - De Gruyter Proceedings in Mathematics

SP - 171

EP - 180

BT - Monte Carlo Methods and Applications

A2 - Sabelfeld, Karl K.

A2 - Dimov, Ivan

PB - De Gruyter

ER -