Uncertainty Characterization Using Copulas (UC)2

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0147-14-C-7903
Agency Tracking Number: B13B-001-0041
Amount: $99,975.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: MDA13-T001
Solicitation Number: 2013.B
Timeline
Solicitation Year: 2013
Award Year: 2014
Award Start Date (Proposal Award Date): 2014-05-08
Award End Date (Contract End Date): 2014-12-08
Small Business Information
1 Van de Graaff Drive, Suite 107, Burlington, MA, -
DUNS: 965530517
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 Onur Ozedemir
 Sr. Research Scientist
 (617) 583-5730
 onur.ozdemir@bostonfusion.com
Business Contact
 Kendra Moore
Title: President
Phone: (617) 583-5730
Email: kendra.moore@bostonfusion.com
Research Institution
 Syracuse University
 Pramod K Varshney
 113 Bowne Hall
Syracuse, NY, 13244-
 (315) 443-4013
 Nonprofit college or university
Abstract
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)

* Information listed above is at the time of submission. *

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