Fusion of multiple estimates by covariance intersection: Why and how it is suboptimal

Ajgl, J. ; Straka, O.

International Journal of Applied Mathematics and Computer Science, Tome 28 (2018) no. 3, pp. 521-530.

Résumé

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.

Détail

Keywords: decentralised estimation, fusion under unknown correlations, covariance intersection, Minkowski sum
Mots-clés : estymacja zdecentralizowana, iloczyn kowariancji, suma Minkowskiego

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