Multi-Phenomenology Discrimination for Feature Aided Data Fusion

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0147-13-C-7316
Agency Tracking Number: B122-005-0270
Amount: $149,998.00
Phase: Phase I
Program: SBIR
Awards Year: 2013
Solicitation Year: 2012
Solicitation Topic Code: MDA12-005
Solicitation Number: 2012.2
Small Business Information
1235 South Clark Street, Suite 400, Arlington, VA, -
DUNS: 036593457
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 David Fiske
 Director, Applied Mathematics
 (703) 414-5036
 david.fiske@dac.us
Business Contact
 Dana Ho
Title: Contracts Manager
Phone: (703) 414-5016
Email: dana.ho@dac.us
Research Institution
 Stub
Abstract
We propose to apply a proprietary discrimination technique rooted in the manifold learning literature to discrimination of object type through radar and through electro-optical/infrared sensors, and to use the features computed by this technique to help correlate tracks between sensors. Our discrimination technique is data-type agnostic, meaning that we can apply the same basic algorithm in both phenomenologies, which, in turn, suggests that future work may allow us to more self-consistently perform cross-sensor data fusion. The proposal leverages prior investment by MDA in radar discrimination techniques and is endorsed by a major MDA prime contractor for sensor technologies, increasing its probability of successfully transitioning to the operation BMDS.

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

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