Adaptive Fleet Synthetic Scenario Research

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$99,962.00
Award Year:
2010
Program:
STTR
Phase:
Phase I
Contract:
N00014-10-M-0291
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, 92106
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
186435830
Principal Investigator:
John Helewa
Senior Systems Engineer
(619) 523-1763
helewa@kablab.com
Business Contact:
John Theriault
CFO
(619) 523-1763
jt@kablab.com
Research Institution:
University of California, San Diego
Robert R Bitmead
Department of Mechanical and A
9500 Gilman Drive
La Jolla, CA, 92093
(858) 822-3477
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 class room. 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 research will show that the use of static models and companion correlation modules during scenario creation will reduce the complexity of scenario development and reduce the domain knowledge required. Static models can be devised to encapsulate domain knowledge for a particular data source, and correlation can be used to fuse the output from each static model to produce a cohesive scenario. To enable autonomic generation and regeneration of multi-source scenarios, the proposed research will also address service composibility and data heterogeneity among the participating static models and correlation modules. Finally, the proposed research will investigate human-computer interfaces for guiding the scenario developer through the autonomic process.

* information listed above is at the time of submission.

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