@inproceedings{ce9e78e1c2c446ceb10fb82cdbfb9e73,

title = "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 is introduced that 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.",

keywords = "Generalized coherence, Maximum entropy, Multiple-channel detection, Sensor networks, Value of information",

author = "Douglas Cochran and Howard, {S. D.} and B. Moran and Schmitt, {H. A.}",

year = "2012",

doi = "10.1109/SSP.2012.6319686",

language = "English (US)",

isbn = "9781467301831",

series = "2012 IEEE Statistical Signal Processing Workshop, SSP 2012",

pages = "297--300",

booktitle = "2012 IEEE Statistical Signal Processing Workshop, SSP 2012",

note = "2012 IEEE Statistical Signal Processing Workshop, SSP 2012 ; Conference date: 05-08-2012 Through 08-08-2012",

}