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Maximum Entropy Surrogation in Multiple Channel Signal Detection


Abstract Multiple-channel detection is considered in the context of a sensor network where data can be exchanged directly between sensor nodes that share a common edge in the network graph. Optimal statistical tests used for signal source detection with multiple noisy sensors, such as the Generalized Coherence (GC) estimate, use pairwise measurements from every pair of sensors in the network and are thus only applicable when the network graph is completely connected, or when data are accumulated at a common fusion center. This thesis presents and exploits a new method that uses maximum-entropy techniques to estimate measurements between pairs of sensors that are not in direct communication, thereby enabling the use of the GC estimate in incompletely... (more)
Created Date 2014-05
Contributor Crider, Lauren Nicole (Author) / Cochran, Douglas (Thesis Director) / Renaut, Rosemary (Committee Member) / Kosut, Oliver (Committee Member) / Barrett, The Honors College / School of Mathematical and Statistical Sciences
Subject Networked Radar / Multi-channel Detection / Maximum Entropy / Generalized Coherence
Series Academic Year 2013-2014
Type Text
Extent 27 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Collaborating Institutions Barrett, the Honors College
Additional Formats MODS / OAI Dublin Core / RIS


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