Likelihood consensus-based distributed particle filtering with distributed proposal density adaptation

Ondrej Hlinka; Franz Hlawatsch; Petar M. Djurić

doi:10.1109/ICASSP.2012.6288762

Likelihood consensus-based distributed particle filtering with distributed proposal density adaptation

Ondrej Hlinka
, Franz Hlawatsch
, Petar M. Djurić

Electrical Engineering

TU Wien

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

20 Scopus citations

Abstract

We present a consensus-based distributed particle filter (PF) for wireless sensor networks. Each sensor runs a local PF to compute a global state estimate that takes into account the measurements of all sensors. The local PFs use the joint (all-sensors) likelihood function, which is calculated in a distributed way by a novel generalization of the likelihood consensus scheme. A performance improvement (or a reduction of the required number of particles) is achieved by a novel distributed, consensus-based method for adapting the proposal densities of the local PFs. The performance of the proposed distributed PF is demonstrated for a target tracking problem.

Original language	English
Title of host publication	2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages	3869-3872
Number of pages	4
DOIs	https://doi.org/10.1109/ICASSP.2012.6288762
State	Published - 2012
Event	2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan Duration: Mar 25 2012 → Mar 30 2012

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)	1520-6149

Conference

Conference	2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Country/Territory	Japan
City	Kyoto
Period	03/25/12 → 03/30/12

Keywords

Distributed particle filter
distributed proposal density adaptation
likelihood consensus
target tracking
wireless sensor network

Access to Document

10.1109/ICASSP.2012.6288762

Cite this

Hlinka, O., Hlawatsch, F., & Djurić, P. M. (2012). Likelihood consensus-based distributed particle filtering with distributed proposal density adaptation. In 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings (pp. 3869-3872). Article 6288762 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2012.6288762

Hlinka, Ondrej ; Hlawatsch, Franz ; Djurić, Petar M. / Likelihood consensus-based distributed particle filtering with distributed proposal density adaptation. 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings. 2012. pp. 3869-3872 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

@inproceedings{52f68ed09c7d4aaaa9b262fceabd3651,

title = "Likelihood consensus-based distributed particle filtering with distributed proposal density adaptation",

abstract = "We present a consensus-based distributed particle filter (PF) for wireless sensor networks. Each sensor runs a local PF to compute a global state estimate that takes into account the measurements of all sensors. The local PFs use the joint (all-sensors) likelihood function, which is calculated in a distributed way by a novel generalization of the likelihood consensus scheme. A performance improvement (or a reduction of the required number of particles) is achieved by a novel distributed, consensus-based method for adapting the proposal densities of the local PFs. The performance of the proposed distributed PF is demonstrated for a target tracking problem.",

keywords = "Distributed particle filter, distributed proposal density adaptation, likelihood consensus, target tracking, wireless sensor network",

author = "Ondrej Hlinka and Franz Hlawatsch and Djuri{\'c}, \{Petar M.\}",

year = "2012",

doi = "10.1109/ICASSP.2012.6288762",

language = "English",

isbn = "9781467300469",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

pages = "3869--3872",

booktitle = "2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings",

note = "2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 ; Conference date: 25-03-2012 Through 30-03-2012",

}

Hlinka, O, Hlawatsch, F & Djurić, PM 2012, Likelihood consensus-based distributed particle filtering with distributed proposal density adaptation. in 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings., 6288762, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 3869-3872, 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012, Kyoto, Japan, 03/25/12. https://doi.org/10.1109/ICASSP.2012.6288762

Likelihood consensus-based distributed particle filtering with distributed proposal density adaptation. / Hlinka, Ondrej; Hlawatsch, Franz; Djurić, Petar M.
2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings. 2012. p. 3869-3872 6288762 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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

TY - GEN

T1 - Likelihood consensus-based distributed particle filtering with distributed proposal density adaptation

AU - Hlinka, Ondrej

AU - Hlawatsch, Franz

AU - Djurić, Petar M.

PY - 2012

Y1 - 2012

N2 - We present a consensus-based distributed particle filter (PF) for wireless sensor networks. Each sensor runs a local PF to compute a global state estimate that takes into account the measurements of all sensors. The local PFs use the joint (all-sensors) likelihood function, which is calculated in a distributed way by a novel generalization of the likelihood consensus scheme. A performance improvement (or a reduction of the required number of particles) is achieved by a novel distributed, consensus-based method for adapting the proposal densities of the local PFs. The performance of the proposed distributed PF is demonstrated for a target tracking problem.

AB - We present a consensus-based distributed particle filter (PF) for wireless sensor networks. Each sensor runs a local PF to compute a global state estimate that takes into account the measurements of all sensors. The local PFs use the joint (all-sensors) likelihood function, which is calculated in a distributed way by a novel generalization of the likelihood consensus scheme. A performance improvement (or a reduction of the required number of particles) is achieved by a novel distributed, consensus-based method for adapting the proposal densities of the local PFs. The performance of the proposed distributed PF is demonstrated for a target tracking problem.

KW - Distributed particle filter

KW - distributed proposal density adaptation

KW - likelihood consensus

KW - target tracking

KW - wireless sensor network

UR - https://www.scopus.com/pages/publications/84863969228

U2 - 10.1109/ICASSP.2012.6288762

DO - 10.1109/ICASSP.2012.6288762

M3 - Conference contribution

AN - SCOPUS:84863969228

SN - 9781467300469

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 3869

EP - 3872

BT - 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings

T2 - 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012

Y2 - 25 March 2012 through 30 March 2012

ER -

Hlinka O, Hlawatsch F, Djurić PM. Likelihood consensus-based distributed particle filtering with distributed proposal density adaptation. In 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings. 2012. p. 3869-3872. 6288762. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2012.6288762

Likelihood consensus-based distributed particle filtering with distributed proposal density adaptation

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