Sensor Resource Management

Award Information
Agency:
Department of Defense
Branch
Missile Defense Agency
Amount:
$100,000.00
Award Year:
2011
Program:
SBIR
Phase:
Phase I
Contract:
HQ0147-11-C-7615
Agency Tracking Number:
B103-002-0419
Solicitation Year:
2010
Solicitation Topic Code:
MDA10-002
Solicitation Number:
2010.3
Small Business Information
Torch Technologies, Inc.
4035 Chris Drive, Suite C, Huntsville, AL, -
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
122515708
Principal Investigator:
Diane Lane
Principal Investigator
(256) 319-6000
diane.lane@torchtechnologies.com
Business Contact:
Kenneth Lones
Director of Contracts
(256) 319-6019
kenneth.lones@torchtechnologies.com
Research Institution:
Stub




Abstract
Torch proposes an innovative data fusion approach based upon a processing and communications architecture which supports a real-time Sensor Resource Management (SRM) driven demonstration of enhanced Track Quality (TQ) for BMDS System Track. Our approach is based upon our experience with the development of real-time networking and sensor algorithms which support significant improvements in TQ based upon the testing and management of sensor resources to enhance track accuracy. Our sensor level TQ models are driven by radar and optical sensor bias model state and covariance estimates coupled with sensor measurement precision and track models. Torch real-time SRM data/message support within our architecture will facilitate the prioritization and transmission of sensor cues to handle the dynamic launch and intercept environment leading up to key functions such as kill assessment. To support a short term planning cycle capability, Torch will address SRM computational loads via the proper choice of sensor, system, and feature based performance models in addition to the use of highly efficient assignment algorithms in a parallel processing architecture. Our Phase I work plan focuses on the implementation of our prototype real-time SRM architecture as well as establishing appropriate sensor resource models and network messaging to demonstrate an improved TQ.

* information listed above is at the time of submission.

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