Acceptance probability of IP-MCMC-PF: revisited

Fernando Iglesias Garcia; Melanie Bocquel; Pranab K. Mandal; Hans Driessen

doi:10.1109/SDF.2015.7347699

Acceptance probability of IP-MCMC-PF: revisited

Fernando Iglesias Garcia, Melanie Bocquel, Pranab K. Mandal, Hans Driessen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

5 Downloads (Pure)

Abstract

Tracking of multiple objects via particle filtering faces the difficulty of dealing effectively with high dimensional state spaces. One efficient solution consists of integrating Markov chain Monte Carlo (MCMC) sampling at the core of the particle filter. To accomplish such integration, a few different approaches have been proposed in the literature during the last decade. In this paper, we introduce the derivation of the acceptance probability of the interacting population MCMC particle filter (IP-MCMC-PF), one of the most recent approaches to MCMC-based particle filtering. Additionally, we show that the previous expression known in the literature was incomplete.

Original language	Undefined
Title of host publication	10th Workshop: Sensor Data Fusion: Trends, Solutions, Applications
Place of Publication	Bonn
Publisher	IEEE
Pages	1-6
Number of pages	6
ISBN (Print)	978-1-4673-7175-9
DOIs	https://doi.org/10.1109/SDF.2015.7347699
Publication status	Published - 6 Oct 2015
Event	Sensor Data Fusion: Trends, Solutions, Applications, SDF 2015 - Bonn, Germany Duration: 6 Oct 2015 → 8 Oct 2015

Publication series

Name
Publisher	IEEE

Workshop

Workshop	Sensor Data Fusion: Trends, Solutions, Applications, SDF 2015
Abbreviated title	SDF
Country/Territory	Germany
City	Bonn
Period	6/10/15 → 8/10/15

Keywords

EWI-26724
MCMC
IR-99227
Particle filter
METIS-315543
Tracking

Access to Document

10.1109/SDF.2015.7347699

http://eprints.eemcs.utwente.nl/secure2/26724/01/07347699.pdf

Cite this

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title = "Acceptance probability of IP-MCMC-PF: revisited",

abstract = "Tracking of multiple objects via particle filtering faces the difficulty of dealing effectively with high dimensional state spaces. One efficient solution consists of integrating Markov chain Monte Carlo (MCMC) sampling at the core of the particle filter. To accomplish such integration, a few different approaches have been proposed in the literature during the last decade. In this paper, we introduce the derivation of the acceptance probability of the interacting population MCMC particle filter (IP-MCMC-PF), one of the most recent approaches to MCMC-based particle filtering. Additionally, we show that the previous expression known in the literature was incomplete.",

keywords = "EWI-26724, MCMC, IR-99227, Particle filter, METIS-315543, Tracking",

author = "{Iglesias Garcia}, Fernando and Melanie Bocquel and Mandal, {Pranab K.} and Hans Driessen",

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AU - Iglesias Garcia, Fernando

AU - Bocquel, Melanie

AU - Mandal, Pranab K.

AU - Driessen, Hans

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PY - 2015/10/6

Y1 - 2015/10/6

N2 - Tracking of multiple objects via particle filtering faces the difficulty of dealing effectively with high dimensional state spaces. One efficient solution consists of integrating Markov chain Monte Carlo (MCMC) sampling at the core of the particle filter. To accomplish such integration, a few different approaches have been proposed in the literature during the last decade. In this paper, we introduce the derivation of the acceptance probability of the interacting population MCMC particle filter (IP-MCMC-PF), one of the most recent approaches to MCMC-based particle filtering. Additionally, we show that the previous expression known in the literature was incomplete.

AB - Tracking of multiple objects via particle filtering faces the difficulty of dealing effectively with high dimensional state spaces. One efficient solution consists of integrating Markov chain Monte Carlo (MCMC) sampling at the core of the particle filter. To accomplish such integration, a few different approaches have been proposed in the literature during the last decade. In this paper, we introduce the derivation of the acceptance probability of the interacting population MCMC particle filter (IP-MCMC-PF), one of the most recent approaches to MCMC-based particle filtering. Additionally, we show that the previous expression known in the literature was incomplete.

KW - EWI-26724

KW - MCMC

KW - IR-99227

KW - Particle filter

KW - METIS-315543

KW - Tracking

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DO - 10.1109/SDF.2015.7347699

M3 - Conference contribution

SN - 978-1-4673-7175-9

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BT - 10th Workshop: Sensor Data Fusion: Trends, Solutions, Applications

PB - IEEE

CY - Bonn

T2 - Sensor Data Fusion: Trends, Solutions, Applications, SDF 2015

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