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Extended object tracking with convolution particle filtering

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Extended object tracking with convolution particle filtering. / Angelova, D.; Mihaylova, L.; Petrov, N.; Gning, A.

Intelligent Systems (IS), 2012 6th IEEE International Conference. IEEE, 2012. p. 96-101.

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

Harvard

Angelova, D, Mihaylova, L, Petrov, N & Gning, A 2012, Extended object tracking with convolution particle filtering. in Intelligent Systems (IS), 2012 6th IEEE International Conference. IEEE, pp. 96-101. https://doi.org/10.1109/IS.2012.6335120

APA

Angelova, D., Mihaylova, L., Petrov, N., & Gning, A. (2012). Extended object tracking with convolution particle filtering. In Intelligent Systems (IS), 2012 6th IEEE International Conference (pp. 96-101). IEEE. https://doi.org/10.1109/IS.2012.6335120

Vancouver

Angelova D, Mihaylova L, Petrov N, Gning A. Extended object tracking with convolution particle filtering. In Intelligent Systems (IS), 2012 6th IEEE International Conference. IEEE. 2012. p. 96-101 https://doi.org/10.1109/IS.2012.6335120

Author

Angelova, D. ; Mihaylova, L. ; Petrov, N. ; Gning, A. / Extended object tracking with convolution particle filtering. Intelligent Systems (IS), 2012 6th IEEE International Conference. IEEE, 2012. pp. 96-101

Bibtex

@inproceedings{e8d0154e0c594b5fad39d57648ba5a06,
title = "Extended object tracking with convolution particle filtering",
abstract = "This paper proposes a sequential Monte Carlo filter (particle filter) for state and parameter estimation of dynamic systems. It is applied to the problem of extended object tracking in the presence of dense clutter. The unknown length of a stick-shape object is estimated in addition to the kinematic parameters. The kernel density estimation technique is utilised to approximate the joint posterior density of target state and static size parameters. The convolution particle filtering approach is validated on a Poisson model for the measurements, originating from the target and clutter. Examples illustrating the filter performance are presented. Simulation results show that the convolution particle filter provides accurate on-line tracking, with very good estimates both for the target kinematic states and for the parameters of the target extent.",
author = "D. Angelova and L. Mihaylova and N. Petrov and A. Gning",
year = "2012",
month = "9",
day = "1",
doi = "10.1109/IS.2012.6335120",
language = "English",
isbn = "978-1-4673-2276-8",
pages = "96--101",
booktitle = "Intelligent Systems (IS), 2012 6th IEEE International Conference",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Extended object tracking with convolution particle filtering

AU - Angelova, D.

AU - Mihaylova, L.

AU - Petrov, N.

AU - Gning, A.

PY - 2012/9/1

Y1 - 2012/9/1

N2 - This paper proposes a sequential Monte Carlo filter (particle filter) for state and parameter estimation of dynamic systems. It is applied to the problem of extended object tracking in the presence of dense clutter. The unknown length of a stick-shape object is estimated in addition to the kinematic parameters. The kernel density estimation technique is utilised to approximate the joint posterior density of target state and static size parameters. The convolution particle filtering approach is validated on a Poisson model for the measurements, originating from the target and clutter. Examples illustrating the filter performance are presented. Simulation results show that the convolution particle filter provides accurate on-line tracking, with very good estimates both for the target kinematic states and for the parameters of the target extent.

AB - This paper proposes a sequential Monte Carlo filter (particle filter) for state and parameter estimation of dynamic systems. It is applied to the problem of extended object tracking in the presence of dense clutter. The unknown length of a stick-shape object is estimated in addition to the kinematic parameters. The kernel density estimation technique is utilised to approximate the joint posterior density of target state and static size parameters. The convolution particle filtering approach is validated on a Poisson model for the measurements, originating from the target and clutter. Examples illustrating the filter performance are presented. Simulation results show that the convolution particle filter provides accurate on-line tracking, with very good estimates both for the target kinematic states and for the parameters of the target extent.

U2 - 10.1109/IS.2012.6335120

DO - 10.1109/IS.2012.6335120

M3 - Conference contribution/Paper

SN - 978-1-4673-2276-8

SP - 96

EP - 101

BT - Intelligent Systems (IS), 2012 6th IEEE International Conference

PB - IEEE

ER -