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Lower Bounds In Estimation Fusion With Partial Knowledge of Correlations

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F21%3A43962470" target="_blank" >RIV/49777513:23520/21:43962470 - isvavai.cz</a>

  • Result on the web

    <a href="https://dx.doi.org/10.1109/MFI52462.2021.9591173" target="_blank" >https://dx.doi.org/10.1109/MFI52462.2021.9591173</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/MFI52462.2021.9591173" target="_blank" >10.1109/MFI52462.2021.9591173</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Lower Bounds In Estimation Fusion With Partial Knowledge of Correlations

  • Original language description

    Mean square error matrices belong to key concepts in decentralised estimation. They assess the quality of estimates and are essential for the optimisation of the estimation fusion. In the case of a missing knowledge, sets of admissible matrices are replaced by their bounds and a robust fusion is applied. This paper prospects a specific partial knowledge of the sets of matrices. Upper bounds are constructed first. Then, the stress is laid on non-zero lower bounds, which do not exist in the fusion under completely unknown correlation. Limit cases are discussed on numerical examples and graphical illustrations are given.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20205 - Automation and control systems

Result continuities

  • Project

    <a href="/en/project/GC20-06054J" target="_blank" >GC20-06054J: Intelligent Distributed Estimation Architectures</a><br>

  • Continuities

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

Others

  • Publication year

    2021

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Proceedings of the 2021 IEEE International Conference on Multisensor Fusion and Integration (MFI 2021)

  • ISBN

    978-1-66544-521-4

  • ISSN

  • e-ISSN

  • Number of pages

    6

  • Pages from-to

    1-6

  • Publisher name

    IEEE

  • Place of publication

    Karlsruhe, Germany

  • Event location

    Karlsruhe, Německo

  • Event date

    Sep 23, 2021

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article