Maximum-entropy Surrogation in Network Signal Detection

Abstract

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.