Collaborative sequential state estimation under unknown heterogeneous noise covariance matrices

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F20%3A00532181" target="_blank" >RIV/67985556:_____/20:00532181 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9195780" target="_blank" >https://ieeexplore.ieee.org/document/9195780</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP.2020.3023823" target="_blank" >10.1109/TSP.2020.3023823</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Collaborative sequential state estimation under unknown heterogeneous noise covariance matrices

Popis výsledku v původním jazyce

We study the problem of distributed sequential estimation of common states and measurement noise covariance matrices of hidden Markov models by networks of collaborating nodes. We adopt a realistic assumption that the true covariance matrices are possibly different (heterogeneous) across the network. This setting is frequent in many distributed real-world systems where the sensors (e.g., radars) are deployed in a spatially anisotropic environment, or where the networks may consist of sensors with different measuring principles (e.g., using different wavelengths). Our solution is rooted in the variational Bayesian paradigm. In order to improve the estimation performance, the measurements and the posterior estimates are communicated among adjacent neighbors within one network hop distance using the information diffusion strategy. The resulting adaptive algorithm selects neighbors with compatible information to prevent degradation of estimates.

Název v anglickém jazyce

Collaborative sequential state estimation under unknown heterogeneous noise covariance matrices

Popis výsledku anglicky

We study the problem of distributed sequential estimation of common states and measurement noise covariance matrices of hidden Markov models by networks of collaborating nodes. We adopt a realistic assumption that the true covariance matrices are possibly different (heterogeneous) across the network. This setting is frequent in many distributed real-world systems where the sensors (e.g., radars) are deployed in a spatially anisotropic environment, or where the networks may consist of sensors with different measuring principles (e.g., using different wavelengths). Our solution is rooted in the variational Bayesian paradigm. In order to improve the estimation performance, the measurements and the posterior estimates are communicated among adjacent neighbors within one network hop distance using the information diffusion strategy. The resulting adaptive algorithm selects neighbors with compatible information to prevent degradation of estimates.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

<a href="/cs/project/GA20-27939S" target="_blank" >GA20-27939S: Bayesovské metody pro nelineární slepé inverzní úlohy</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Signal Processing

ISSN

1053-587X

e-ISSN

—

Svazek periodika

68

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

5365-5378

Kód UT WoS článku

000574739900010

EID výsledku v databázi Scopus

2-s2.0-85092572093