Uncertainty Characterization Using Copulas (UC)2

Boston Fusion, together with our teammate Syracuse University, propose a program of research and development, Uncertainty Characterization Using Copulas (UC)2, that will result in a parametric framework based on the statistical theory of copulas for modeling uncertainties for the problem of object classification. (UC)2 will produce a mathematical framework, founded on rigorous theoretical analysis, along with algorithms and software that will accurately characterize uncertainties associated with different sensor outputs. (UC)2 will naturally lead to (1) a better understanding of both the performance and the limits of the underlying fusion architecture; (2) new and enhanced fusion algorithms with optimal or near-optimal performance under realistic operating conditions; and (3) recommendations for sensor or feature selection under system resource constraints. In Phase I, we developed the initial framework and demonstrated the feasibility of (UC)2 on real data. In Phase II, we will demonstrate the performance of the proposed approach via a high-fidelity testbed environment, reflecting the operational system requirements. Approved for Public Release, 15-MDA-8303 (1 July 15)