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Paper details
Number 3 - September 2018
Volume 28 - 2018
Fusion of multiple estimates by covariance intersection: Why and how it is suboptimal
Jiři Ajgl, Ondřej Straka
Abstract
The fusion under unknown correlations tunes a combination of local estimates in such a way that upper bounds of the
admissible mean square error matrices are optimised. Based on the recently discovered relation between the admissible
matrices and Minkowski sums of ellipsoids, the optimality of existing algorithms is analysed. Simple examples are used to
indicate the reasons for the suboptimality of the covariance intersection fusion of multiple estimates. Further, an extension
of the existing family of upper bounds is proposed, which makes it possible to get closer to the optimum, and a general case
is discussed. All results are obtained analytically and illustrated graphically.
Keywords
decentralised estimation, fusion under unknown correlations, covariance intersection, Minkowski sum