Fusion of Multiple Estimates by Covariance Intersection: Why and Howit Is Suboptimal

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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.

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