Innovative Technologies to Optimize Embedded Training & Simulation

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$99,988.00
Award Year:
1998
Program:
SBIR
Phase:
Phase I
Contract:
n/a
Award Id:
41471
Agency Tracking Number:
41471
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
360 State Route 101, Suite 201, Bedford, NH, 03110
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
Dr. John Ebersole
(603) 472-6686
Business Contact:
() -
Research Institution:
n/a
Abstract
We are proposing an exploratory study to investigate the feasibility of applying innovative fuzzy logic analysis and genetic algorithm technologies to embedded training (ET) and embedded simulation (ES) . We propose to investigate the application of these technologies to optimizing registration of live/virtual objects and generation of information superiority training scenarios. Information superiority is one of the goals of the military of tomorrow. A warfighter is confronted with situations that border on chaos. Speed, tension, and complexity all impact a warfighter's performance (and thus a mission's Chances of success). One of the major issues facing a warfighter is the fact that the information he/she receives comes from various sources, and these sources may produce outputs that are in conflict with each other. Such a condition is known as information dissonance. Learning to deal with information dissonance and leveraging of information superiority are the keys to attaining situation awareness. Helping the warfighter to experience and attain situation awareness through ET/ES can be viewed as a means of preparing the warfighter to experience and attain situation awareness on the real battlefield. The training of the warfighter, then, must reflect these battlefield conditions and make use of available information in a similar way. Our proposal also addresses this key need of future ET/ES systems. BENEFITS: Any situation in which a human must learn to resolve conflicting or dissonant information from multiple sources in a time-critical fashion stands to benefit from our proposed approach. These situations include, but are not limited to. image analysis (intelligence and reconnaissance) , medical imaging and diagnosis, aerospace (both military and commercial) , scheduling, and resource allocation.

* information listed above is at the time of submission.

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