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Combining Model-based Reasoning with Knowledge Discovery Techniques for Level 2 and 3 Fusion

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-05-M-0207
Agency Tracking Number: N054-019-0140
Amount: $69,792.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N05-T019
Solicitation Number: N/A
Timeline
Solicitation Year: 2005
Award Year: 2005
Award Start Date (Proposal Award Date): 2005-08-01
Award End Date (Contract End Date): 2006-05-31
Small Business Information
625 Mount Auburn Street
Cambridge, MA 02138
United States
DUNS: 115243701
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Subrata Das
 Chief Scientist
 (617) 491-3474
 sdas@cra.com
Business Contact
 Paul Gonsalves
Title: Vice President
Phone: (617) 491-3474
Email: pgonsalves@cra.com
Research Institution
 UNIV. OF MIAMI
 Kamal Premaratne
 
Electrical & Computer Eng Dept, 1251 Memorial Drive, #EB406
Coral Gables, FL 33146
United States

 (305) 284-4051
 Nonprofit College or University
Abstract

We propose to develop an approach to combine model based reasoning with knowledge discovery techniques for enhanced Level 2 and 3 data fusion, especially suitable for detecting asymmetric threats (e.g. ambush, insurgency) in cluttered urban environments. The knowledge discovery part: 1) deploys evidence filtering of large volumes of intelligence data to detect low-signature significant spatio-temporal events; and 2) uses clustering to perform spatial and time-series analysis of messages without requiring semantic information in the data. The former, for example, detects and tracks isolated suspicious vehicles, whereas the latter detects spatially correlated moving units over time within urban environments. Detected events and patterns trigger the need for assessing newly developed situations and threats, resulting in invocations of doctrine-based static and dynamic Bayesian belief network (BN) models that are causal and graphical in nature, and are well known for handling uncertainty. The selected BN models then perform higher-level data fusion based on other observables propagated as evidence into the models, by taking into account varying credibility and confidence of information sources via the Dempster-Shafer (D-S) theory of belief functions. The proposed hybrid approach will significantly enhance the fusion capability of DCGS-MC and C2PC for Marine Corps operations in urban environments.

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

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