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Radar Debris Algorithms and Models for Discrimination

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: W9113M-07-C-0204
Agency Tracking Number: B074-004-0029
Amount: $99,998.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: MDA07-T004
Solicitation Number: N/A
Solicitation Year: 2007
Award Year: 2007
Award Start Date (Proposal Award Date): 2007-08-30
Award End Date (Contract End Date): 2008-02-29
Small Business Information
20 New England Business Center
Andover, MA 01810
United States
DUNS: 073800062
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Edward Rietman
 Principal Research Scientist
 (978) 689-0003
Business Contact
 B. Green
Title: President, PSI R&D Operations
Phone: (978) 689-0003
Research Institution
 C. J Grant
Applied Physics Laboratory 11100 Johns Hopkins Road
Laurel, MD 20723-6099
United States

 (240) 228-5000
 Nonprofit college or university

We are proposing an innovative approach to sensor fusion and target recognition based on transductive inference with support vector machines. Our proposed algorithm will apply innovative discrimination to the fusion of sensor (feature) and contextual scenario information through the development of robust algorithms and software necessary to collect, process, and fuse information from multiple sources (radars either at the same or different frequencies as well as EO/IR sensor assets). This proposed approach has the advantage of being able to combine these disparate data types of high dimensionality and use small training sets to achieve unprecedented performance. The goal of the data fusion process described in this proposal is to operate on a combination of sensor measurements, features, track states, and object type and identification likelihoods to produce a highly accurate integrated picture of the battlespace. Our results will be compared with Professor Donald Maurer's work at Johns Hopkins Applied Physics Laboratory, where he conducted research on using Bayesian network for the same task.

* Information listed above is at the time of submission. *

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