  In this paper we describe General Covariance Union (GCU) and show that
solutions to GCU and the Minimum Enclosing Ellipsoid (MEE) problems are
equivalent. This is a surprising result because GCU is defined over positive
semidefinite (PSD) matrices with statistical interpretations while MEE involves
PSD matrices with geometric interpretations. Their equivalence establishes an
intersection between the seemingly disparate methodologies of covariance-based
(e.g., Kalman) filtering and bounded region approaches to data fusion.
