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Preemptive Actions with Dynamic Anticipatory Targeting (PREDATAR)

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-07-C-4508
Agency Tracking Number: F061-051-0717
Amount: $1,121,790.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: AF06-051
Solicitation Number: 2006.1
Timeline
Solicitation Year: 2006
Award Year: 2007
Award Start Date (Proposal Award Date): 2007-02-12
Award End Date (Contract End Date): 2009-02-12
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
 David Lawless
 Senior Software Engineer
 (617) 491-3474
 dlawless@cra.com
Business Contact
 Paul Gonsalves
Title: Vice President
Phone: (617) 491-3474
Email: pgonsalves@cra.com
Research Institution
N/A
Abstract

A target type prediction system dubbed PREDATAR has been designed under a Phase I effort, to provide evolving probabilistic identifications of ground targets nominated as Time Sensitive Targets (TSTs), well in advance of their positive identification (PID). For each TST, a probabilistic identification vector of potential target types is successively refined via a five-step ‘pipelined’ process: 1) expert rules provide a ‘quick expert guess’ about the TST target type; 2) a target ontology is used to determine which target types are feasible; 3) credibility checks of target type versus reporting ISR platform are performed to eliminate obvious errors; 4) learning about target types is leveraged to adapt to new operational situations; and 5) the most likely TST target types are vetted using encapsulated expert knowledge. The viability of the proposed approach was studied in the context of a post-high intensity scenario. Implementation of a PREDATAR prototype has been initiated, based on an in-house Bayesian Belief Network engine. Under Phase II, implementation and validation will be completed, by bringing in additional tools and technologies related to ontology development, decision tree learning, and rule-based inferencing. Future plans include transitioning PREDATAR to existing and ongoing DoD efforts, including ADOCS, TBMCS, GCCS, and DCGS.

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

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