Effects of network topology on the conditional distributions of surrogated generalized coherence estimates

Coherence estimation is an established approach in multiple-channel detection and estimation, providing optimal solutions in many cases. Recent work has considered the use of maximum-entropy matrix completion when elements are missing from the gram matrix from which the coherence statistics are formed. This is desirable in sensor network settings, for example, where direct communication is not available between every pair of nodes in the network. This paper examines the role of network topology in determining the conditional distributions of the statistic obtained by the matrix completion process under both signal-present and signal-absent hypotheses.