Distributed Bernoulli Filtering Using Likelihood Consensus

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Distributed Bernoulli Filtering Using Likelihood Consensus

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Distributed Bernoulli Filtering Using Likelihood Consensus

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/GA17-19638S" target="_blank" >GA17-19638S: Určování pohybu arteriální stěny pomocí sekvenčního bayesovského odhadu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Signal and Information Processing over Networks

ISSN

2373-776X

e-ISSN

—

Svazek periodika

5

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

218-233

Kód UT WoS článku

000467571500002

EID výsledku v databázi Scopus

—