Multi-Phenomenology Discrimination for Feature Aided Data Fusion

Award Information
Agency:
Department of Defense
Branch
Missile Defense Agency
Amount:
$149,998.00
Award Year:
2013
Program:
SBIR
Phase:
Phase I
Contract:
HQ0147-13-C-7316
Award Id:
n/a
Agency Tracking Number:
B122-005-0270
Solicitation Year:
2012
Solicitation Topic Code:
MDA12-005
Solicitation Number:
2012.2
Small Business Information
1235 South Clark Street, Suite 400, Arlington, VA, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
036593457
Principal Investigator:
David Fiske
Director, Applied Mathematics
(703) 414-5036
david.fiske@dac.us
Business Contact:
Dana Ho
Contracts Manager
(703) 414-5016
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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