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Particle algorithms for filtering in high dimensional state space : a case study in group object tracking.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

Published

Standard

Particle algorithms for filtering in high dimensional state space : a case study in group object tracking. / Mihaylova, Lyudmila; Carmi, Avishy.

Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Prague : IEEE, 2011. p. 5932-5935.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

Harvard

Mihaylova, L & Carmi, A 2011, Particle algorithms for filtering in high dimensional state space : a case study in group object tracking. in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, Prague, pp. 5932-5935. <http://www.icassp2011.com/en/welcome>

APA

Mihaylova, L., & Carmi, A. (2011). Particle algorithms for filtering in high dimensional state space : a case study in group object tracking. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5932-5935). IEEE. http://www.icassp2011.com/en/welcome

Vancouver

Mihaylova L, Carmi A. Particle algorithms for filtering in high dimensional state space : a case study in group object tracking. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Prague: IEEE. 2011. p. 5932-5935

Author

Mihaylova, Lyudmila ; Carmi, Avishy. / Particle algorithms for filtering in high dimensional state space : a case study in group object tracking. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Prague : IEEE, 2011. pp. 5932-5935

Bibtex

@inbook{eab671e620784c8d8ce26611f347fe88,
title = "Particle algorithms for filtering in high dimensional state space : a case study in group object tracking.",
abstract = "We briefly present the current state-of-the-art approaches for group and extended object tracking with an emphasis on particle methods which have high potential to handle complex structured scenarios. The big dimensionality attributed to the group tracking problem poses a major difficulty to particle filters (PFs). This in turn has motivated researchers to devise many alternatives and variants over the past decade. In this work, we corroborate and extend a single promising direction for alleviating the dimensionality problem. Our derived scheme endows a recently introduced Markov chain Monte Carlo (MCMC) PF algorithm with an improved proposal distribution. We demonstrate the performance of our approach using a nonlinear system with up to 40 states.",
keywords = "nonlinear estimation, sequential Monte Carlo methods, Markov chain Monte Carlo methods (MCMC), high dimensional systems, group object tracking",
author = "Lyudmila Mihaylova and Avishy Carmi",
year = "2011",
month = may,
day = "24",
language = "English",
isbn = "978-1-4577-0537-3",
pages = "5932--5935",
booktitle = "Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
publisher = "IEEE",

}

RIS

TY - CHAP

T1 - Particle algorithms for filtering in high dimensional state space : a case study in group object tracking.

AU - Mihaylova, Lyudmila

AU - Carmi, Avishy

PY - 2011/5/24

Y1 - 2011/5/24

N2 - We briefly present the current state-of-the-art approaches for group and extended object tracking with an emphasis on particle methods which have high potential to handle complex structured scenarios. The big dimensionality attributed to the group tracking problem poses a major difficulty to particle filters (PFs). This in turn has motivated researchers to devise many alternatives and variants over the past decade. In this work, we corroborate and extend a single promising direction for alleviating the dimensionality problem. Our derived scheme endows a recently introduced Markov chain Monte Carlo (MCMC) PF algorithm with an improved proposal distribution. We demonstrate the performance of our approach using a nonlinear system with up to 40 states.

AB - We briefly present the current state-of-the-art approaches for group and extended object tracking with an emphasis on particle methods which have high potential to handle complex structured scenarios. The big dimensionality attributed to the group tracking problem poses a major difficulty to particle filters (PFs). This in turn has motivated researchers to devise many alternatives and variants over the past decade. In this work, we corroborate and extend a single promising direction for alleviating the dimensionality problem. Our derived scheme endows a recently introduced Markov chain Monte Carlo (MCMC) PF algorithm with an improved proposal distribution. We demonstrate the performance of our approach using a nonlinear system with up to 40 states.

KW - nonlinear estimation

KW - sequential Monte Carlo methods

KW - Markov chain Monte Carlo methods (MCMC)

KW - high dimensional systems

KW - group object tracking

M3 - Chapter

SN - 978-1-4577-0537-3

SP - 5932

EP - 5935

BT - Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

PB - IEEE

CY - Prague

ER -