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A Monte Carlo Algorithm for State and Parameter Estimation of Extended Targets

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

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A Monte Carlo Algorithm for State and Parameter Estimation of Extended Targets. / Angelova, D.; Mihaylova, L.
ICCS'06: Proceedings of the 6th international conference on Computational Science. 2006. p. 624-631.

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

Harvard

Angelova, D & Mihaylova, L 2006, A Monte Carlo Algorithm for State and Parameter Estimation of Extended Targets. in ICCS'06: Proceedings of the 6th international conference on Computational Science. pp. 624-631, 6th International Conference on Computational Science, Reading, 28/05/06. <http://www.springerlink.com/content/l876gk0j0v564g13/>

APA

Angelova, D., & Mihaylova, L. (2006). A Monte Carlo Algorithm for State and Parameter Estimation of Extended Targets. In ICCS'06: Proceedings of the 6th international conference on Computational Science (pp. 624-631) http://www.springerlink.com/content/l876gk0j0v564g13/

Vancouver

Angelova D, Mihaylova L. A Monte Carlo Algorithm for State and Parameter Estimation of Extended Targets. In ICCS'06: Proceedings of the 6th international conference on Computational Science. 2006. p. 624-631

Author

Angelova, D. ; Mihaylova, L. / A Monte Carlo Algorithm for State and Parameter Estimation of Extended Targets. ICCS'06: Proceedings of the 6th international conference on Computational Science. 2006. pp. 624-631

Bibtex

@inproceedings{de63f86cdee0492a9d29d9717c2a42ad,
title = "A Monte Carlo Algorithm for State and Parameter Estimation of Extended Targets",
abstract = "This paper considers the joint state and parameter estimation of extended targets. Both the target kinematic states, position and speed, are estimated with the target extent parameters. The developed algorithm is applied to a ship, whose shape is modelled by an ellipse. A Bayesian sampling algorithm with finite mixtures is proposed for the evaluation of the extent parameters whereas a suboptimal Bayesian interacting multiple model (IMM) filter estimates the kinematic parameters of the maneuvering ship. The algorithm performance is evaluated by Monte Carlo comparison with a particle filtering approach.",
keywords = "extended objects, nonlinear systems, state and parameter estimation, stochastic simulation, DCS-publications-id, inproc-435, DCS-publications-credits, dsp-fa, DCS-publications-personnel-id, 121",
author = "D. Angelova and L. Mihaylova",
note = "V.N. Alexandrov et al. (Eds.), ICCS 2006, part III, LNCS Proceedings 3993, pp. 624-631, Springer-Verlag Berlin Heidelberg, May 28-31, 2006. ISBN: 978-3-540-34383-7 DOI: 10.1007/11758532_82; 6th International Conference on Computational Science ; Conference date: 28-05-2006 Through 31-05-2006",
year = "2006",
month = may,
day = "28",
language = "English",
pages = "624--631",
booktitle = "ICCS'06: Proceedings of the 6th international conference on Computational Science",

}

RIS

TY - GEN

T1 - A Monte Carlo Algorithm for State and Parameter Estimation of Extended Targets

AU - Angelova, D.

AU - Mihaylova, L.

N1 - V.N. Alexandrov et al. (Eds.), ICCS 2006, part III, LNCS Proceedings 3993, pp. 624-631, Springer-Verlag Berlin Heidelberg, May 28-31, 2006. ISBN: 978-3-540-34383-7 DOI: 10.1007/11758532_82

PY - 2006/5/28

Y1 - 2006/5/28

N2 - This paper considers the joint state and parameter estimation of extended targets. Both the target kinematic states, position and speed, are estimated with the target extent parameters. The developed algorithm is applied to a ship, whose shape is modelled by an ellipse. A Bayesian sampling algorithm with finite mixtures is proposed for the evaluation of the extent parameters whereas a suboptimal Bayesian interacting multiple model (IMM) filter estimates the kinematic parameters of the maneuvering ship. The algorithm performance is evaluated by Monte Carlo comparison with a particle filtering approach.

AB - This paper considers the joint state and parameter estimation of extended targets. Both the target kinematic states, position and speed, are estimated with the target extent parameters. The developed algorithm is applied to a ship, whose shape is modelled by an ellipse. A Bayesian sampling algorithm with finite mixtures is proposed for the evaluation of the extent parameters whereas a suboptimal Bayesian interacting multiple model (IMM) filter estimates the kinematic parameters of the maneuvering ship. The algorithm performance is evaluated by Monte Carlo comparison with a particle filtering approach.

KW - extended objects

KW - nonlinear systems

KW - state and parameter estimation

KW - stochastic simulation

KW - DCS-publications-id

KW - inproc-435

KW - DCS-publications-credits

KW - dsp-fa

KW - DCS-publications-personnel-id

KW - 121

M3 - Conference contribution/Paper

SP - 624

EP - 631

BT - ICCS'06: Proceedings of the 6th international conference on Computational Science

T2 - 6th International Conference on Computational Science

Y2 - 28 May 2006 through 31 May 2006

ER -