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Population based particle filtering

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

Published

Standard

Population based particle filtering. / Bhaskar, H.; Mihaylova, L.; Maskell, Simon.

IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, 2008 . IEEE, 2008. p. 31-38.

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

Harvard

Bhaskar, H, Mihaylova, L & Maskell, S 2008, Population based particle filtering. in IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, 2008 . IEEE, pp. 31-38, The Institution of Engineering and Technology Seminar on Target Tracking and Data Fusion: Algorithms and Applications, Birmingham, UK, 15/04/08.

APA

Bhaskar, H., Mihaylova, L., & Maskell, S. (2008). Population based particle filtering. In IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, 2008 (pp. 31-38). IEEE.

Vancouver

Bhaskar H, Mihaylova L, Maskell S. Population based particle filtering. In IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, 2008 . IEEE. 2008. p. 31-38

Author

Bhaskar, H. ; Mihaylova, L. ; Maskell, Simon. / Population based particle filtering. IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, 2008 . IEEE, 2008. pp. 31-38

Bibtex

@inproceedings{4cfb7ce6a349461690730c3451cc707f,
title = "Population based particle filtering",
abstract = "This paper proposes a novel particle filtering strategy by combining population Monte Carlo Markov chain methods with sequential Monte Carlo chain particle which we call evolving population Monte Carlo Markov Chain (EP MCMC) filtering. Iterative convergence on groups of particles (populations) is obtained using a specified kernel moving particles toward more likely regions. The proposed technique introduces variety in the particles both in the sampling procedure and in the resampling step. The proposed EP MCMC filter is compared with the generic particle filter [1], with a population MCMC [2] and a sequential Monte Carlo sam- pler [2]. Its effectiveness is illustrated over an example for object tracking in video sequences and over the bearing only tracking problem.",
keywords = "tracking, particle filter, population based methods, MCMC, SMC sampler",
author = "H. Bhaskar and L. Mihaylova and Simon Maskell",
note = "Published by the Institution of Engineering and Technology, London, ISBN 9780863419102, ISSN 0537-9989 Printed in Great Britain by Page Bros Ltd. Reference PES08273",
year = "2008",
month = "4",
day = "15",
language = "English",
isbn = "978-0-86341-910-2",
pages = "31--38",
booktitle = "IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, 2008",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Population based particle filtering

AU - Bhaskar, H.

AU - Mihaylova, L.

AU - Maskell, Simon

N1 - Published by the Institution of Engineering and Technology, London, ISBN 9780863419102, ISSN 0537-9989 Printed in Great Britain by Page Bros Ltd. Reference PES08273

PY - 2008/4/15

Y1 - 2008/4/15

N2 - This paper proposes a novel particle filtering strategy by combining population Monte Carlo Markov chain methods with sequential Monte Carlo chain particle which we call evolving population Monte Carlo Markov Chain (EP MCMC) filtering. Iterative convergence on groups of particles (populations) is obtained using a specified kernel moving particles toward more likely regions. The proposed technique introduces variety in the particles both in the sampling procedure and in the resampling step. The proposed EP MCMC filter is compared with the generic particle filter [1], with a population MCMC [2] and a sequential Monte Carlo sam- pler [2]. Its effectiveness is illustrated over an example for object tracking in video sequences and over the bearing only tracking problem.

AB - This paper proposes a novel particle filtering strategy by combining population Monte Carlo Markov chain methods with sequential Monte Carlo chain particle which we call evolving population Monte Carlo Markov Chain (EP MCMC) filtering. Iterative convergence on groups of particles (populations) is obtained using a specified kernel moving particles toward more likely regions. The proposed technique introduces variety in the particles both in the sampling procedure and in the resampling step. The proposed EP MCMC filter is compared with the generic particle filter [1], with a population MCMC [2] and a sequential Monte Carlo sam- pler [2]. Its effectiveness is illustrated over an example for object tracking in video sequences and over the bearing only tracking problem.

KW - tracking

KW - particle filter

KW - population based methods

KW - MCMC

KW - SMC sampler

M3 - Conference contribution/Paper

SN - 978-0-86341-910-2

SP - 31

EP - 38

BT - IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, 2008

PB - IEEE

ER -