Innovative approaches to Situation Modeling, Threat Modeling and Threat Prediction

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$149,999.00
Award Year:
2012
Program:
SBIR
Phase:
Phase I
Contract:
FA8750-12-C-0163
Award Id:
n/a
Agency Tracking Number:
O113-DR5-1182
Solicitation Year:
2011
Solicitation Topic Code:
OSD11-DR5
Solicitation Number:
2011.3
Small Business Information
1643 Hemlock Wy, Broomfield, CO, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
130770055
Principal Investigator:
ChristopherBowman
Owner
(303) 469-9828
cbowman@df-nn.com
Business Contact:
ChristopherBowman
Owner
(303) 469-9828
cbowman@df-nn.com
Research Institute:
Stub




Abstract
The DF & NN team is composed of Christopher Bowman, Charles Morefield, Alan Steinberg, and Ed Waltz. We will develop methods to model and characterize the quality of data that has been"re-purposed"for fusion applications. We will develop algorithms useful to High-Level Information Fusion (HLF), primarily the areas of situation modeling, threat modeling, and threat prediction. Our algorithms will specifically address bias and uncertainty when data sources include non-numeric qualitative measurements. Our focus is on methods that automatically learn to characterize such re-used/re-purposed data, thereby avoiding expensive off-line manually constructed data and model transformations. The HLF design will apply the Dual Node Network Data Fusion & Resource Management (DF & RM) technical architecture. We propose to cost effectively construct models of bias and error which will over time provide estimates of these errors. Technical objectives are: 1. Scenarios that expose appropriate HLF design and data uncertainty issues to fusion system development. Ontologies will include red/blue force strategies/tactics representing military/political Courses of Action 2. Multi-model approach to L2/3 HLF providing situation modeling, threat modeling, and threat prediction. 3. Develop algorithmic approaches for modeling re-purposed data, especially estimating biases and errors. 4. Deliver a software architectural design for HLF, using the DNN DF & RM technical architecture.

* information listed above is at the time of submission.

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