Advanced Feature Aided Track and CDI Fusion Processing of Data from

Award Information
Agency:
Department of Defense
Amount:
$98,729.00
Program:
SBIR
Contract:
HQ0006-07-C-7734
Solitcitation Year:
2006
Solicitation Number:
2006.3
Branch:
Missile Defense Agency
Award Year:
2007
Phase:
Phase I
Agency Tracking Number:
B063-004-0559
Solicitation Topic Code:
MDA06-004
Small Business Information
INTELLIGENT SYSTEMS RESEARCH, INC.
2984 Eagles Claw Avenue, Thousand Oaks, CA, 91362
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Duns:
068577175
Principal Investigator
 Phillip Dennis
 Senior Scientist
 (805) 493-5279
 Phillip.Dennis@isrweb.com
Business Contact
 William Cook
Title: President
Phone: (805) 493-5279
Email: William.Cook@isrweb.com
Research Institution
N/A
Abstract
Successful missile defense operations require an accurate real-time single integrated battlespace picture, while minimizing data throughput and bandwidth requirements. Current multi-sensor tracking approaches that treat kinematic and feature fusion as separate processes have high rates of correlation and target typing errors. Our integrated fusion process integrates optical and radar data, thereby reducing battlespace picture uncertainty by jointly considering kinematic, radar features, and thermal imaging data. Networked sensors working jointly substantially increase lethal object identification capability by sharing independent 2-D or 3-D kinematic data and feature data such as RCS, IR signatures, and length profiles. This approach improves system performance by increasing the ability to resolve high speed closely spaced objects, track maneuvering objects (boosting missiles and maneuvering/unstable reentry vehicles), and identify and destroy lethal objects within dense target clusters containing decoys and debris. Intelligent Systems Research will develop an integrated and distributed target type fusion process within a distributed component system architecture that receives statistically independent data from distributed network sensors and simultaneously processes this data. Our approach operates on sensor reports containing features, tracklets, and object type likelihoods. We minimize network throughput by adaptively transmitting data with a high value-of-information according to the needs of network participants.

* information listed above is at the time of submission.

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