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A Novel Sequential Monte Carlo Approach for Extended Object Tracking Based on Border Parameterisation

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

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A Novel Sequential Monte Carlo Approach for Extended Object Tracking Based on Border Parameterisation. / Petrov, Nikolay; Mihaylova, Lyudmila; Gning, Amadou et al.
14th International Conference on Information Fusion: ISIF. Chicago, Illinois, USA, 2011. p. 306-313.

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

Harvard

Petrov, N, Mihaylova, L, Gning, A & Angelova, D 2011, A Novel Sequential Monte Carlo Approach for Extended Object Tracking Based on Border Parameterisation. in 14th International Conference on Information Fusion: ISIF. Chicago, Illinois, USA, pp. 306-313. <http://www.fusion2011.org/>

APA

Petrov, N., Mihaylova, L., Gning, A., & Angelova, D. (2011). A Novel Sequential Monte Carlo Approach for Extended Object Tracking Based on Border Parameterisation. In 14th International Conference on Information Fusion: ISIF (pp. 306-313). http://www.fusion2011.org/

Vancouver

Petrov N, Mihaylova L, Gning A, Angelova D. A Novel Sequential Monte Carlo Approach for Extended Object Tracking Based on Border Parameterisation. In 14th International Conference on Information Fusion: ISIF. Chicago, Illinois, USA. 2011. p. 306-313

Author

Petrov, Nikolay ; Mihaylova, Lyudmila ; Gning, Amadou et al. / A Novel Sequential Monte Carlo Approach for Extended Object Tracking Based on Border Parameterisation. 14th International Conference on Information Fusion: ISIF. Chicago, Illinois, USA, 2011. pp. 306-313

Bibtex

@inproceedings{d07a00f9c9984739a7e63da3c885d4e2,
title = "A Novel Sequential Monte Carlo Approach for Extended Object Tracking Based on Border Parameterisation",
abstract = "Extended objects are characterised with multiple measurements originated from different locations of the object surface. This paper presents a novel Sequential Monte Carlo (SMC) approach for extended object tracking based on border parametrisation. The problem is formulated for general nonlinear problems. The main contribution of this work is in the derivation of the likelihood function for nonlinear measurement functions, with sets of measurements belonging to a bounded region. Simulation results are presented when the object is surrounded by a circular region. Accurate estimation results are presented both for the object kinematic state and object extent.",
keywords = "sequential Monte Carlo methods, measurement uncertainty, nonlinear estimation",
author = "Nikolay Petrov and Lyudmila Mihaylova and Amadou Gning and Donka Angelova",
year = "2011",
month = jul,
language = "English",
pages = "306--313",
booktitle = "14th International Conference on Information Fusion",

}

RIS

TY - GEN

T1 - A Novel Sequential Monte Carlo Approach for Extended Object Tracking Based on Border Parameterisation

AU - Petrov, Nikolay

AU - Mihaylova, Lyudmila

AU - Gning, Amadou

AU - Angelova, Donka

PY - 2011/7

Y1 - 2011/7

N2 - Extended objects are characterised with multiple measurements originated from different locations of the object surface. This paper presents a novel Sequential Monte Carlo (SMC) approach for extended object tracking based on border parametrisation. The problem is formulated for general nonlinear problems. The main contribution of this work is in the derivation of the likelihood function for nonlinear measurement functions, with sets of measurements belonging to a bounded region. Simulation results are presented when the object is surrounded by a circular region. Accurate estimation results are presented both for the object kinematic state and object extent.

AB - Extended objects are characterised with multiple measurements originated from different locations of the object surface. This paper presents a novel Sequential Monte Carlo (SMC) approach for extended object tracking based on border parametrisation. The problem is formulated for general nonlinear problems. The main contribution of this work is in the derivation of the likelihood function for nonlinear measurement functions, with sets of measurements belonging to a bounded region. Simulation results are presented when the object is surrounded by a circular region. Accurate estimation results are presented both for the object kinematic state and object extent.

KW - sequential Monte Carlo methods

KW - measurement uncertainty

KW - nonlinear estimation

M3 - Conference contribution/Paper

SP - 306

EP - 313

BT - 14th International Conference on Information Fusion

CY - Chicago, Illinois, USA

ER -