Adaptive Fleet Synthetic Scenario Research

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$493,212.00
Award Year:
2012
Program:
STTR
Phase:
Phase II
Contract:
N66001-12-C-5231
Agency Tracking Number:
N10A-044-0725
Solicitation Year:
2010
Solicitation Topic Code:
N10A-T044
Solicitation Number:
2010.A
Small Business Information
KAB LABORATORIES INC.
1110 Rosecrans Street, #203, San Diego, CA, -
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
186435830
Principal Investigator:
John Helewa
Principal Engineer
(619) 523-1763
helewa@kablab.com
Business Contact:
John Theriault
Chief Financial Officer
(619) 523-1763
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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