A Distributed Bernoulli Filter Based on Likelihood Consensus with Adaptive Pruning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F18%3APU140254" target="_blank" >RIV/00216305:26220/18:PU140254 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/8455302" target="_blank" >https://ieeexplore.ieee.org/document/8455302</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
A Distributed Bernoulli Filter Based on Likelihood Consensus with Adaptive Pruning
Original language description
The Bernoulli filter (BF) is a Bayes-optimal method for target tracking when the target can be present or absent in unknown time intervals and the measurements are affected by clutter and missed detections. We propose a distributed particle-based multisensor BF algorithm that approximates the centralized multisensor BF for arbitrary nonlinear and non-Gaussian system models. Our distributed algorithm uses a new extension of the likelihood consensus (LC) scheme that accounts for both target presence and absence and includes an adaptive pruning of the LC expansion coefficients. Simulation results for a heterogeneous sensor network with significant noise and clutter show that the performance of our algorithm is close to that of the centralized multisensor BF.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA17-19638S" target="_blank" >GA17-19638S: Sequential Bayesian Estimation of Arterial Wall Motion</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2018 21st International Conference on Information Fusion (FUSION)
ISBN
978-0-9964527-6-2
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
2445-2452
Publisher name
IEEE
Place of publication
NEW YORK
Event location
Cambridge, UK
Event date
Jul 10, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000495071900335