Distributed Object Discrimination for BMD

Award Information
Agency:
Department of Defense
Amount:
$97,968.00
Program:
SBIR
Contract:
FA8750-04-C-0199
Solitcitation Year:
2004
Solicitation Number:
2004.1
Branch:
Missile Defense Agency
Award Year:
2004
Phase:
Phase I
Agency Tracking Number:
B041-011-1026
Solicitation Topic Code:
MDA04-011
Small Business Information
ALPHATECH, INC.
6 New England Executive Park, Burlington, MA, 01803
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Duns:
094841665
Principal Investigator
 Chee-Yee Chong
 Chief Scientist
 (858) 812-7874
 cchong@alphatech.com
Business Contact
 John Barry
Title: Contracts Manager
Phone: (781) 273-3388
Email: jbarry@alphatech.com
Research Institution
N/A
Abstract
Object discrimination is one of the most important functions in ballistic missile defense since successful engagement and intercept of the warhead requires timely discrimination of lethal objects from decoys and other non-lethal objects. Multiple sensors exploiting diversity in phenomenology and viewing geometry can provide better discrimination information than a single sensor but exploiting this information requires a good fusion algorithm. Although centralized discrimination is theoretically optimal, distributed discrimination has advantages of lower communication bandwidth, robustness to failure, etc. The proposed research addresses key issues in distributed discrimination including choice of appropriate architecture, information to communicate among processing agents, optimal fusion algorithms, and communication strategies. It adopts the information graph model to analyze the dependence among processing agents so that information will not be double-counted. Object and sensor models represented by Bayesian networks are used to identify the minimum sufficient information that should be communicated and fused. An information-theoretic approach is used to decide when communication should take place. The Phase I effort will demonstrate the technical feasibility of distributed discrimination by integrating these components and comparing the resulting performance with that using centralized discrimination.

* information listed above is at the time of submission.

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