Uncertainty Characterization Using Copulas (UC)2

Boston Fusion, together with our teammate Syracuse University, propose a program of research and development, namely Uncertainty Characterization Using Copulas, which will lead to a parametric framework based on the statistical theory of copulas for modeling uncertainties in a centralized fusion architecture for the problem of target classification for ballistic missile defense applications. This program of research and development will produce a mathematical framework, founded on a rigorous theoretical analysis, which will result in accurate characterization of the uncertainties associated with different sensor outputs. This framework will naturally lead to: (1) a better understanding of both the performance and the limits of the underlying fusion architecture; (2) new ways for developing fusion algorithms with optimal or near-optimal performance; and (3) recommendations for sensor or feature selection under system resource constraints. In Phase I, we will develop a prototype system design based on a challenge problem to validate the feasibility of the proposed statistical modeling approach. The resulting modeling framework will not only help in characterizing the performance of the fusion architecture, but will also enable future development of new fusion algorithms with enhanced performance in Phase II. Approved for Public Release 14-MDA-7663 (8 January 14)