Collaborative sequential state estimation under unknown heterogeneous noise covariance matrices
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Collaborative sequential state estimation under unknown heterogeneous noise covariance matrices
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20202 - Communication engineering and systems
Result continuities
Project
<a href="/en/project/GA20-27939S" target="_blank" >GA20-27939S: Bayesian methods for non-linear blind inverse problems</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Signal Processing
ISSN
1053-587X
e-ISSN
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Volume of the periodical
68
Issue of the periodical within the volume
10
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
5365-5378
UT code for WoS article
000574739900010
EID of the result in the Scopus database
2-s2.0-85092572093