Dual Approach to Inverse Covariance Intersection Fusion

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43973066" target="_blank" >RIV/49777513:23520/24:43973066 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/MFI62651.2024.10705759" target="_blank" >https://doi.org/10.1109/MFI62651.2024.10705759</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/MFI62651.2024.10705759" target="_blank" >10.1109/MFI62651.2024.10705759</a>

Result language
angličtina
Original language name
Dual Approach to Inverse Covariance Intersection Fusion
Original language description
Linear fusion of estimates under the condition of no knowledge of correlation of estimation errors has reached maturity. On the other hand, various cases of partial knowledge are still active research areas. A frequent motivation is to deal with “common information” or “common noise”, whatever it means. A fusion rule for a strict meaning of the former expression has already been elaborated. Despite the dual relationship, a strict meaning of the latter one has not been considered so far. The paper focuses on this area. The assumption of unknown “common noise” is formulated first, analysis of theoretical properties and illustrations follow. Although the results are disappointing from the perspective of a single upper bound of mean square error matrices, the partial knowledge demonstrates improvement over no knowledge in suboptimal cases and from the perspective of families of upper bounds.
Czech name
—
Czech description
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Project
<a href="/en/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotics and advanced industrial production</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
ISBN
979-8-3503-6803-1
ISSN
2835-947X
e-ISSN
2767-9357
Number of pages
6
Pages from-to
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Publisher name
IEEE
Place of publication
Plzeň
Event location
Plzeň, Česká republika
Event date
Sep 4, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

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