PROJECT TITLE :

Minimum Covariance Bounds for the Fusion under Unknown Correlations

ABSTRACT:

One in all the key challenges in distributed linear estimation is the systematic fusion of estimates. Whereas the fusion gains that minimize the mean squared error of the fused estimate for known correlations have been established, no analogous statement might be obtained thus far for unknown correlations. In this contribution, we have a tendency to derive the gains that minimize the certain on the true covariance of the fused estimate and prove that Covariance Intersection (CI) is the optimal bounding algorithm for two estimates under utterly unknown correlations. When combining 3 or additional variables, the CI equations are not necessarily optimal, as shown by a counterexample.