  Multiple-channel detection is considered in the context of a sensor network
where raw data are shared only by nodes that have a common edge in the network
graph. Established multiple-channel detectors, such as those based on
generalized coherence or multiple coherence, use pairwise measurements from
every pair of sensors in the network and are thus directly applicable only to
networks whose graphs are completely connected. An approach introduced here
uses a maximum-entropy technique to formulate surrogate values for missing
measurements corresponding to pairs of nodes that do not share an edge in the
network graph. The broader potential merit of maximum-entropy baselines in
quantifying the value of information in sensor network applications is also
noted.
