Radar Debris Algorithms and Models for Discrimination

Award Information
Agency:
Department of Defense
Branch
Missile Defense Agency
Amount:
$995,548.00
Award Year:
2008
Program:
SBIR
Phase:
Phase II
Contract:
W9113M-08-C-0139
Agency Tracking Number:
B063-014-0264
Solicitation Year:
2006
Solicitation Topic Code:
MDA06-014
Solicitation Number:
2006.3
Small Business Information
TECHNOLOGY SERVICE CORP.
1900 S. Sepulveda Blvd, Suite 300, Los Angeles, CA, 90025
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
053885604
Principal Investigator:
George Bohannon
Corporate Senior Staff
(310) 954-2200
george.bohannon@tsc.com
Business Contact:
Robert Graziano
President
(203) 268-1249
robert.graziano@tsc.com
Research Institution:
n/a
Abstract
Radar data collected on U.S. domestic missile flight tests has revealed significant debris environments. Solid rocket boosters have been found to create particularly dense debris environments, but deployment hardware debris is typically also present, both for solid and liquid fuel boosters. Data collected on foreign tests has also indicated significant debris environments. It is important that the effects of debris on BMDS performance be well understood and its detrimental effects mitigated where necessary. The debris RCS modeling technique developed under this SBIR will help ensure that large-scale BMDS simulations accurately reflect realistic debris environments. Also, the prescreening technique makes it possible to quickly assign debris identification probabilities to many of the objects in a missile complex, typically after a few seconds of track, thereby enabling the defense to allocate its resources more effectively. In Phase I TSC demonstrated the feasibility of a debris RCS modeling technique. TSC will refine and fully automate the model in Phase II. The Phase I effort also generated quantitative prescreener performance results for a limited set of deployment and debris environment conditions. A much more expanded set of conditions will be examined during Phase II to mature the algorithm and characterize performance.

* information listed above is at the time of submission.

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