Battlefield Management and Expert Cueing (BFMEC) System Prototyping

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$599,993.00
Award Year:
1996
Program:
SBIR
Phase:
Phase II
Contract:
n/a
Award Id:
28905
Agency Tracking Number:
28905
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
American Gnc Corp (Currently American GNC Corporation)
Po Box 10987, Canoga Park, CA, 91304
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
Dr. Ching-fang Lin
(818) 407-0092
Business Contact:
() -
Research Institution:
n/a
Abstract
This Phase I proposal is intended to develop an advanced Battlefield Management and Expert Cueing (BFMEC) System using distributed expert system decision aids for direct/indirect fire. The BFMEC is aimed at small units (such as an artillery battery) and will incorporate recent technological breakthroughs in decision aids. The BFMEC system is centered on the advanced blackboard model employing dynamic multiple knowledge sources capable of eliminating the traditional knowledge acquisition bottleneck arising from conventional expert systems processing and providing an on-line learning and new knowledge acquisition under stressing operating scenarios. A conceptual design of specific expert system modules, including hardware implementation and software prototyping environment will be developed. The proposed concept is expected to provide the following enhanced performance features: (1) automated decision aiding techniques with feedback from previous decisions; (2) eliminate the bottleneck of knowledge acquisition and computational flow; (3) effectively handle the asynchronous distributed detection and hybrid events problem via multi-reasoning strategies; (4) innovatively acquire new knowledge, update incorrect rules, and "enrich" the existing knowledge base; and (5) provide high quality decision solutions to the commander with sophisticated situation assessment for perfect target identification and weapon to target assignment. Our efforts are complemented by the design of full target acquisition and classification algorithm suites that allow realistic evaluation of the developed situation awareness and decision aids expert systems within the context of the actual performance potential of information providing sensors and sources.

* information listed above is at the time of submission.

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