A Game Theoretic Approach for Threat Prediction and Situation Awareness

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$70,000.00
Award Year:
2005
Program:
STTR
Phase:
Phase I
Contract:
N00014-05-M-0205
Agency Tracking Number:
N054-019-0310
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
Intelligent Automation, Inc.
15400 Calhoun Drive, Suite 400, Rockville, MD, 20855
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
161911532
Principal Investigator:
Genshe Chen
Research Electrical Engineer
(301) 294-5218
gchen@i-a-i.com
Business Contact:
Mark James
Contracts and Proposals Manager
(301) 294-5221
mjames@i-a-i.com
Research Institution:
UNIV. OF NEVADA
Carl G Looney
College of Engineering
Reno, NV, 89554
(775) 784-6974
Nonprofit college or university
Abstract
Intelligent Automation, Inc. (IAI), and its sub-contactor, Professor Carl G. Looney from University of Nevada propose a highly innovative approach for Level 2+ information fusion using hybrid data fusion with adversarial Markov game, named a Game Theoretic Approach for Threat Prediction and Situation Awareness. The primary goal is to investigate and demonstrate the effectiveness of Markov game theory and the advanced knowledge infrastructures for Level 2+ information fusion, such as Situation Assessment (Refinement) and Threat Assessment (Refinement) and so on, therefore improve the capabilities of battlefield situation awareness. To achieve this goal, first, a hybrid data fusion approach is proposed to apply in Situation Refinement to perform spatial and temporal processing on tracks produced by Level 1 multi-sensor, multi-target track fusion, supplemented with intelligence information from both structured data sources such as databases and unstructured data sources such as ontology-based documents. Second, ontology-based information representation is proposed for building a Virtual Battlespace with less computational complexity in Level 2 fusion. Third, an adversarial Markov game framework is proposed for Threat Refinement to drive existing and newly formulated models of threat behavior with factlets derived from Situation Refinement to support the determination of possible enemy courses of actions.

* information listed above is at the time of submission.

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