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Box Particle Filtering for Extended Object Tracking

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

Published

Standard

Box Particle Filtering for Extended Object Tracking. / Petrov, Nikolay; Gning, Amadou; Mihaylova, Lyudmila; Angelova, D.

Information Fusion (FUSION), 2012 15th International Conference on. IEEE, 2012. p. 82-89.

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

Harvard

Petrov, N, Gning, A, Mihaylova, L & Angelova, D 2012, Box Particle Filtering for Extended Object Tracking. in Information Fusion (FUSION), 2012 15th International Conference on. IEEE, pp. 82-89. <http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6289790>

APA

Petrov, N., Gning, A., Mihaylova, L., & Angelova, D. (2012). Box Particle Filtering for Extended Object Tracking. In Information Fusion (FUSION), 2012 15th International Conference on (pp. 82-89). IEEE. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6289790

Vancouver

Petrov N, Gning A, Mihaylova L, Angelova D. Box Particle Filtering for Extended Object Tracking. In Information Fusion (FUSION), 2012 15th International Conference on. IEEE. 2012. p. 82-89

Author

Petrov, Nikolay ; Gning, Amadou ; Mihaylova, Lyudmila ; Angelova, D. / Box Particle Filtering for Extended Object Tracking. Information Fusion (FUSION), 2012 15th International Conference on. IEEE, 2012. pp. 82-89

Bibtex

@inproceedings{e2f560338be04389a0e62f13eca4dbd6,
title = "Box Particle Filtering for Extended Object Tracking",
abstract = "This paper focuses on real-time tracking of an extended object in the presence of clutter. This task reduces to the estimation of the object kinematic state and its extent, based on multiple measurements originated from the same object. A solution to this challenging problem is presented within the recently proposed Box Particle Filtering framework. The Box Particle Filter replaces the point samples with regions, which we call boxes. The performance of the Box Particle Filter for extended object tracking is studied over a challenging scenario with simulated cluttered radar measurements, consisting of range and bearing components. The efficiency is evaluated for different levels of clutter, number of box particles, uncertainty regions for the measurements, number of the active sensors collecting the measurements data and iterations for the contraction of the uncertainty region. Accurate estimation results are demonstrated.",
keywords = "particle filtering, extended objects, Box Particle filters, nonlinear systems, state and parameter estimation",
author = "Nikolay Petrov and Amadou Gning and Lyudmila Mihaylova and D Angelova",
year = "2012",
month = jul,
day = "7",
language = "English",
isbn = "978-1-4673-0417-7",
pages = "82--89",
booktitle = "Information Fusion (FUSION), 2012 15th International Conference on",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Box Particle Filtering for Extended Object Tracking

AU - Petrov, Nikolay

AU - Gning, Amadou

AU - Mihaylova, Lyudmila

AU - Angelova, D

PY - 2012/7/7

Y1 - 2012/7/7

N2 - This paper focuses on real-time tracking of an extended object in the presence of clutter. This task reduces to the estimation of the object kinematic state and its extent, based on multiple measurements originated from the same object. A solution to this challenging problem is presented within the recently proposed Box Particle Filtering framework. The Box Particle Filter replaces the point samples with regions, which we call boxes. The performance of the Box Particle Filter for extended object tracking is studied over a challenging scenario with simulated cluttered radar measurements, consisting of range and bearing components. The efficiency is evaluated for different levels of clutter, number of box particles, uncertainty regions for the measurements, number of the active sensors collecting the measurements data and iterations for the contraction of the uncertainty region. Accurate estimation results are demonstrated.

AB - This paper focuses on real-time tracking of an extended object in the presence of clutter. This task reduces to the estimation of the object kinematic state and its extent, based on multiple measurements originated from the same object. A solution to this challenging problem is presented within the recently proposed Box Particle Filtering framework. The Box Particle Filter replaces the point samples with regions, which we call boxes. The performance of the Box Particle Filter for extended object tracking is studied over a challenging scenario with simulated cluttered radar measurements, consisting of range and bearing components. The efficiency is evaluated for different levels of clutter, number of box particles, uncertainty regions for the measurements, number of the active sensors collecting the measurements data and iterations for the contraction of the uncertainty region. Accurate estimation results are demonstrated.

KW - particle filtering

KW - extended objects

KW - Box Particle filters

KW - nonlinear systems

KW - state and parameter estimation

M3 - Conference contribution/Paper

SN - 978-1-4673-0417-7

SP - 82

EP - 89

BT - Information Fusion (FUSION), 2012 15th International Conference on

PB - IEEE

ER -