Adaptive Fleet Synthetic Scenario Research

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N66001-12-C-5231
Agency Tracking Number: N10A-044-0725
Amount: $493,212.00
Phase: Phase II
Program: STTR
Awards Year: 2012
Solicitation Year: 2010
Solicitation Topic Code: N10A-T044
Solicitation Number: 2010.A
Small Business Information
1110 Rosecrans Street, #203, San Diego, CA, -
DUNS: 186435830
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 John Helewa
 Principal Engineer
 (619) 523-1763
 helewa@kablab.com
Business Contact
 John Theriault
Title: Chief Financial Officer
Phone: (619) 523-1763
Email: jt@kablab.com
Research Institution
 University of California San Diego
 Robert Bitmead
 9500 Gilman Drive
UCSD
La Jolla, CA 92093, CA, 92093-0214
 (858) 822-3234
 Nonprofit college or university
Abstract
Synthetic scenario-based training of Navy personnel in the use of Navy SIGINT/IO systems has helped to reduce training costs, and it has enabled the personnel to be trained in an environment that sufficiently approximates real-world situations that could not otherwise be accomplished within the classroom. However, scenario development is highly complex and involves a great deal of human effort and domain knowledge, discouraging the modification of existing scenarios to keep them current in an ever-changing threat environment. This problem is exacerbated when the scenario represents a combination of multiple data sources. The proposed Phase II effort will leverage the positive results from the Phase I research to develop a fieldable Scenario Generator able to output Stallion-ready training scenarios. The Scenario Generator will make use of data-driven static models developed during Phase I, which significantly reduced scenario creation time and reduced the domain knowledge required. The Phase I research showed that domain knowledge, encapsulated within selected data source, could be used to drive static models, and that those static models could be orchestrated such that their output produces a cohesive, multiple-Intelligence (Multi-INT) scenario.

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

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