Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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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
T2 - The Institution of Engineering and Technology Seminar on Target Tracking and Data Fusion: Algorithms and Applications
Y2 - 15 April 2008 through 16 April 2008
ER -