Direct versus prediction-based particle filter algorithms

François Desbouvries; Boujemaa Ait-El-Fquih

doi:10.1109/MLSP.2008.4685497

Direct versus prediction-based particle filter algorithms

François Desbouvries
, Boujemaa Ait-El-Fquih

CNRS SAMOVAR UMR 5157

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

Particle Filtering (PF) algorithms propagate in time a Monte Carlo (MC) approximation of the a posteriori filtering measure in a Hidden Markov Chain (HMC) model. In this paper we first shed some new light on two classical PF algorithms, which can be considered as natural MC implementations of two two-step direct recursive formulas for computing the filtering distribution. We next address the Particle Prediction (PP) problem, which happens to be simpler than the PF problem because the optimal prediction conditional importance distribution (CID) is much easier to sample from. Motivated by this result we finally develop two PP-based PF algorithms, and we compare our algorithms via simulations.

langue originale	Anglais
titre	Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
Pages	303-308
Nombre de pages	6
Les DOIs	https://doi.org/10.1109/MLSP.2008.4685497
état	Publié - 1 déc. 2008
Modification externe	Oui
Evénement	2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexique Durée: 16 oct. 2008 → 19 oct. 2008

Série de publications

Nom	Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

Une conférence

Une conférence	2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
Pays/Territoire	Mexique
La ville	Cancun
période	16/10/08 → 19/10/08

Accès au document

10.1109/MLSP.2008.4685497

Contient cette citation

@inproceedings{b67497c5fa884e968451a9ac0fc42ffc,

title = "Direct versus prediction-based particle filter algorithms",

abstract = "Particle Filtering (PF) algorithms propagate in time a Monte Carlo (MC) approximation of the a posteriori filtering measure in a Hidden Markov Chain (HMC) model. In this paper we first shed some new light on two classical PF algorithms, which can be considered as natural MC implementations of two two-step direct recursive formulas for computing the filtering distribution. We next address the Particle Prediction (PP) problem, which happens to be simpler than the PF problem because the optimal prediction conditional importance distribution (CID) is much easier to sample from. Motivated by this result we finally develop two PP-based PF algorithms, and we compare our algorithms via simulations.",

keywords = "Hidden Markov chains, Optimal importance function, Particle filtering, Sampling importance resampling, Sequential importance sampling",

author = "Fran{\c c}ois Desbouvries and Boujemaa Ait-El-Fquih",

year = "2008",

month = dec,

day = "1",

doi = "10.1109/MLSP.2008.4685497",

language = "English",

isbn = "9781424423767",

series = "Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008",

pages = "303--308",

booktitle = "Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008",

note = "2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 ; Conference date: 16-10-2008 Through 19-10-2008",

}

Desbouvries, F & Ait-El-Fquih, B 2008, Direct versus prediction-based particle filter algorithms. Dans Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008., 4685497, Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008, p. 303-308, 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008, Cancun, Mexique, 16/10/08. https://doi.org/10.1109/MLSP.2008.4685497

Direct versus prediction-based particle filter algorithms. / Desbouvries, François; Ait-El-Fquih, Boujemaa.
Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008. 2008. p. 303-308 4685497 (Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Direct versus prediction-based particle filter algorithms

AU - Desbouvries, François

AU - Ait-El-Fquih, Boujemaa

PY - 2008/12/1

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N2 - Particle Filtering (PF) algorithms propagate in time a Monte Carlo (MC) approximation of the a posteriori filtering measure in a Hidden Markov Chain (HMC) model. In this paper we first shed some new light on two classical PF algorithms, which can be considered as natural MC implementations of two two-step direct recursive formulas for computing the filtering distribution. We next address the Particle Prediction (PP) problem, which happens to be simpler than the PF problem because the optimal prediction conditional importance distribution (CID) is much easier to sample from. Motivated by this result we finally develop two PP-based PF algorithms, and we compare our algorithms via simulations.

AB - Particle Filtering (PF) algorithms propagate in time a Monte Carlo (MC) approximation of the a posteriori filtering measure in a Hidden Markov Chain (HMC) model. In this paper we first shed some new light on two classical PF algorithms, which can be considered as natural MC implementations of two two-step direct recursive formulas for computing the filtering distribution. We next address the Particle Prediction (PP) problem, which happens to be simpler than the PF problem because the optimal prediction conditional importance distribution (CID) is much easier to sample from. Motivated by this result we finally develop two PP-based PF algorithms, and we compare our algorithms via simulations.

KW - Hidden Markov chains

KW - Optimal importance function

KW - Particle filtering

KW - Sampling importance resampling

KW - Sequential importance sampling

U2 - 10.1109/MLSP.2008.4685497

DO - 10.1109/MLSP.2008.4685497

M3 - Conference contribution

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T3 - Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

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BT - Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

T2 - 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

Y2 - 16 October 2008 through 19 October 2008

ER -

Direct versus prediction-based particle filter algorithms

Résumé

Série de publications

Une conférence

Accès au document

Empreinte digitale

Contient cette citation