Title:	Learning and Exploiting Partial Knowledge in Distributed Estimation
Authors:	Radtke, Sussane Ajgl, Jiří Straka, Ondřej Hanebeck, Uwe D.
Citation:	RADTKE, S. AJGL, J. STRAKA, O. HANEBECK, UD. Learning and Exploiting Partial Knowledge in Distributed Estimation. In Proceedins of the 2021 IEEE International Conference on Multisensor Fusion and Integration (MFI 2021). Karlsruhe: IEEE, 2021. s. 1-7. ISBN: 978-1-66544-521-4 , ISSN: neuvedeno
Issue Date:	2021
Publisher:	IEEE
Document type:	konferenční příspěvek ConferenceObject
URI:	2-s2.0-85122868684 http://hdl.handle.net/11025/47260
ISBN:	978-1-66544-521-4
ISSN:	neuvedeno
Keywords in different language:	estimation fusion;partially known correlation;learning of correlation
Abstract in different language:	In distributed estimation, several sensor nodes provide estimates of the same underlying dynamic process. These estimates are correlated but due to local processing, the correlations are only partially known or even unknown. For a consistent fusion of the local estimates, the correlation needs to be properly treated. Many methods provide consistent but overly conservative fusion results. In this paper, we propose to learn partial knowledge about the correlation in the form of correlation sets and exploit this knowledge to provide less conservative estimates. We use a simple numerical example to demonstrate the advantages of the proposed approach in terms of quality and consistency and how the quality of the fused estimate increases with time.
Rights:	Plný text je přístupný v rámci univerzity přihlášeným uživatelům. © IEEE
Appears in Collections:	Konferenční příspěvky / Conference Papers (KKY) OBD

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