Title:	Lower Bounds In Estimation Fusion With Partial Knowledge of Correlations
Authors:	Ajgl, Jiří Straka, Ondřej
Citation:	AJGL, J. STRAKA, O. Lower Bounds In Estimation Fusion With Partial Knowledge of Correlations. In Proceedings of the 2021 IEEE International Conference on Multisensor Fusion and Integration (MFI 2021). Karlsruhe, Germany: IEEE, 2021. s. 1-6. ISBN: 978-1-66544-521-4 , ISSN: neuvedeno
Issue Date:	2021
Publisher:	IEEE
Document type:	konferenční příspěvek ConferenceObject
URI:	2-s2.0-85122871772 http://hdl.handle.net/11025/47134
ISBN:	978-1-66544-521-4
Keywords in different language:	estimation fusion;partially known correlation;matrix bounds;Covariance Intersection
Abstract in different language:	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.
Rights:	Plný text je přístupný v rámci univerzity přihlášeným uživatelům. © IEEE
Appears in Collections:	Konferenční příspěvky / Conference Papers (KKY) OBD

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