Distributed Bernoulli Filtering Using Likelihood Consensus

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F18%3APU140253" target="_blank" >RIV/00216305:26220/18:PU140253 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/8579567" target="_blank" >https://ieeexplore.ieee.org/document/8579567</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSIPN.2018.2881718" target="_blank" >10.1109/TSIPN.2018.2881718</a>

Result language

angličtina

Original language name

Distributed Bernoulli Filtering Using Likelihood Consensus

Original language description

We consider the detection and tracking of a target in a decentralized sensor network. The presence of the target is uncertain, and the sensor measurements are affected by clutter and missed detections. The state-evolution model and the measurement model may be nonlinear and non-Gaussian. For this practically relevant scenario, we propose a particle-based distributed Bernoulli filter (BF) that provides to each sensor approximations of the Bayes-optimal estimates of the target presence probability and the target state. The proposed method uses all the measurements in the network while requiring only local intersensor communication. This is enabled by an extension of the likelihood consensus method that reaches consensus on the likelihood function under both the target presence and target absence hypotheses. We also propose an adaptive pruning of the likelihood expansion coefficients that yields a significant reduction of intersensor communication. Finally, we present a new variant of the likelihood consensus method that is suited to networks containing star-connected sensor groups. Simulation results show that in challenging scenarios, including a heterogeneous sensor network with significant noise and clutter, the performance of the proposed distributed BF approaches that of the optimal centralized multisensor BE We also demonstrate that the proposed distributed BF outperforms a state-of-the-art distributed BF at the expense of a higher amount of intersensor communication.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20203 - Telecommunications

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)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Transactions on Signal and Information Processing over Networks

ISSN

2373-776X

e-ISSN

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Volume of the periodical

5

Issue of the periodical within the volume

2

Country of publishing house

US - UNITED STATES

Number of pages

16

Pages from-to

218-233

UT code for WoS article

000467571500002

EID of the result in the Scopus database

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