Date of Award

12-2005

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Department of Mathematics and Statistics

First Advisor

Mark E. Oxley, PhD

Abstract

Multisensor data fusion is presented in a rigorous mathematical format, with definitions consistent with the desires of the data fusion community. A model of event-state fusion is developed and described. Definitions of fusion rules and fusors are introduced, along with the functor categories of which they are objects. Defining fusors and competing fusion rules involves the use of an objective function of the researcher's choice. One such objective function, a functional on families of classification systems, and in particular, receiver operating characteristics (ROCs), is introduced. Its use as an objective function is demonstrated in that the argument that minimizes it (a particular ROC) corresponds to the Bayes Optimal Threshold, given certain assumptions. This is proven using a calculus of variations approach using ROC curves as a constraint. This constraint is extended to ROC manifolds, in particular, topological subspaces of ℝn. These optimal points can be found analytically if the closed form of the ROC manifold is known, or is calculated from the functional (as the minimizing argument) when a finite number of points are available for comparison in a family of classification systems. Under different data assumptions, the minimizing argument of the ROC functional is shown to be the point of an ROC manifold corresponding to the Neyman-Pearson criteria. A second functional, the l2 norm, is shown to determine the min-max threshold. A more robust functional is developed from the offered functionals.

AFIT Designator

AFIT-DS-ENC-05-02

DTIC Accession Number

ADA450338

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Included in

Mathematics Commons

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