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Session:Distributed Digital Signal Processing for Sensor Networking
Time:Thursday, May 20, 11:15 - 11:30
Presentation: Special Session Lecture
Topic: Special Sessions: Distributed Digital Signal Processing for Sensor Networking
Authors: Doron Blatt; University of Michigan 
 Alfred O. Hero III; University of Michigan 
Abstract: The problem of finding the maximum likelihood estimator of a commonly observed model, based on data collected by a sensor network under power and bandwidth constraints is considered. In particular, a case where the sensors cannot fully share their data is treated. An iterative algorithm that relaxes the requirement of sharing all the data is given. The algorithm is based on a local Fisher scoring method and an iterative information sharing procedure. The case where the sensors share sub-optimal estimates is also analyzed. The asymptotic distribution of the estimates is derived and used to provide means of discrimination between estimates that are associated with different local maxima of the log-likelihood function. The results are validated by a simulation.

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