Real-time Calibration of Embedded Simulation for Faster than Real-time Course Of Action Analysis (COAA)

Award Information
Agency:
Department of Defense
Amount:
$729,995.00
Program:
SBIR
Contract:
W15P7T-08-C-K404
Solitcitation Year:
2006
Solicitation Number:
2006.2
Branch:
Army
Award Year:
2009
Phase:
Phase II
Agency Tracking Number:
A062-189-0284
Solicitation Topic Code:
A06-189
Small Business Information
GS Engineering, Inc.
47500 US Hwy 41, Houghton, MI, 49931
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Duns:
073282928
Principal Investigator
 Robert McGraw
 Vice-President, CTO
 (858) 677-9207
 rmcgraw@ramlabs.com
Business Contact
 Michelle Jaent
Title: Director of Finance and C
Phone: (858) 677-9207
Email: mjaent@ramlabs.com
Research Institution
N/A
Abstract
The U.S. Army is looking for technologies that better support mission planning and re-planning for forces on-the-move. Existing tools and those under development, such as the Coalition Joint Spectrum Management Planning Tool (CJSMPT), are required to provide on-the-move automated interference and de-confliction analysis in support of mission-related network operations planning amidst the uncertainties presented by forces-on-the move. These tools must also provide processes that off-load portions of the operator’s cognitive workload. The CARET (COA Analysis with Real-time Effects Toolbox) Framework prototype addresses these needs by making use of distributed agents to extract and inject real-time data into embedded simulations for use in evaluating COAs amidst real-time C4ISR effects. This framework provides the capabilities needed by the CJSMPT to consider prediction and uncertainty presented by forces on the move through COA Analysis simulations, corresponding agent-driven faster-than-real-time network analysis, real-time C4I calibrated data updates for consistently evaluating the mission and network state, and reflective cognitive agents to assist network operations planning and re-planning activities in real-time by off-loading portions of the cognitive workload. The Phase I effort has demonstrated several of these basic agents that take into account COA simulation (OneSAF), emitter databases, intelligent agents, and network models (ITM) as a proof-of-concept.

* information listed above is at the time of submission.

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