Covariance Intersection fusion with element-wise partial knowledge of correlation

Covariance Intersection fusion is a linear rule for combining estimates. If the cross-correlation matrix of the errors of two estimates is unknown, the rule is bound-optimal. This paper elaborates the case when some elements of the cross-correlation matrix are known. Techniques for constructing a family of upper bounds of the joint mean square error matrix are introduced. All configurations for the fusion of up to four estimates are considered explicitly. The techniques are also applicable for the fusion of more than four estimates. (C) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper presents a linear rule for covariance intersection fusion and discusses the case when some elements of the cross-correlation matrix are known. It introduces techniques for constructing upper bounds of the joint mean square error matrix and considers explicit configurations for fusing up to four estimates, while also noting their applicability for more than four estimates.