2024-10-05T16:58:20Zhttps://keep.lib.asu.edu/oai/requestoai:keep.lib.asu.edu:node-1371002021-08-11T21:09:57Zoai_pmh:alloai_pmh:repo_items137100
https://hdl.handle.net/2286/R.I.22831
http://rightsstatements.org/vocab/InC/1.0/
2014-05
27 pages
eng
Crider, Lauren Nicole
Cochran, Douglas
Renaut, Rosemary
Kosut, Oliver
Barrett, The Honors College
School of Mathematical and Statistical Sciences
Text
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 connected sensor networks. The research in this thesis culminates in a main conjecture supported by statistical tests regarding the topology of the incomplete network graphs.
Networked Radar
Multi-channel Detection
Maximum Entropy
Generalized Coherence
Maximum Entropy Surrogation in Multiple Channel Signal Detection