Advanced Feature Aided Track and CDI Fusion Processing of Data from...

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0147-08-C-7921
Agency Tracking Number: B063-004-0559
Amount: $1,149,950.00
Phase: Phase II
Program: SBIR
Awards Year: 2008
Solicitation Year: 2006
Solicitation Topic Code: MDA06-004
Solicitation Number: 2006.3
Small Business Information
INTELLIGENT SYSTEMS RESEARCH, INC.
3390 Auto Mall Drive, Thousand Oaks, CA, 91362
DUNS: 068577175
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Phillip Dennis
 Chief Scientist
 (805) 409-0439
 Phillip.Dennis@isrweb.com
Business Contact
 William Cook
Title: President
Phone: (805) 409-0429
Email: William.Cook@isrweb.com
Research Institution
N/A
Abstract
Successful missile defense operations require an accurate real-time single integrated missile picture, while minimizing data throughput and bandwidth requirements. Current fusion approaches treat kinematic and feature fusion as separate processes and have higher rates of correlation errors and target typing errors. Our integrated fusion process reduces these errors by jointly considering both kinematic and feature data. Specifically, this feature aided approach uses data such as RCS, length, and radar signal modulation (RSM) and other features to decrease the probability of incorrect association, improve CDI fusion, reduce false track likelihood, and enhance track accuracy. This approach improves overall system effectiveness 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 designed, developed and tested a prototype proof-of-concept system. This prototype integrated track and CDI fusion process operates within a distributed component architecture that receives statistically independent kinematic and associated feature data from network sensors and simultaneously processes this data. Our approach operates on sensor reports containing feature and kinematic measurements, tracklets, and object type likelihoods.

* information listed above is at the time of submission.

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