A Game Theoretic Approach for Threat Prediction and Situation Awareness

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-05-M-0205
Agency Tracking Number: N054-019-0310
Amount: $70,000.00
Phase: Phase I
Program: STTR
Awards Year: 2005
Solicitation Year: 2005
Solicitation Topic Code: N05-T019
Solicitation Number: N/A
Small Business Information
15400 Calhoun Drive, Suite 400, Rockville, MD, 20855
DUNS: 161911532
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 Genshe Chen
 Research Electrical Engineer
 (301) 294-5218
Business Contact
 Mark James
Title: Contracts and Proposals Manager
Phone: (301) 294-5221
Email: mjames@i-a-i.com
Research Institution
 Carl G Looney
 College of Engineering
Reno, NV, 89554
 (775) 784-6974
 Nonprofit college or university
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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