Adaptive Data Fusion for Real-time Threat Assessment

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$79,999.00
Award Year:
2011
Program:
SBIR
Phase:
Phase I
Contract:
N00167-11-P-0146
Award Id:
n/a
Agency Tracking Number:
N103-224-0500
Solicitation Year:
2010
Solicitation Topic Code:
N103-224
Solicitation Number:
2010.3
Small Business Information
P O Box 1274, Princeton, NJ, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
135270473
Principal Investigator:
Galina Rogova
Senior Research Scientist
(609) 651-4500
rogova@altusystems.com
Business Contact:
Lundy Lewis
President
(609) 651-4500
buford@altusystems.com
Research Institution:
Stub




Abstract
One of the key goals of strengthening maritime security is to increase maritime domain awareness, involving a combination of intelligence, surveillance, and operational information to build as complete a picture as possible to assess the threats and vulnerabilities in the maritime realm. Maintaining coherent situation awareness is essential for making informed timely decisions aimed at detecting and deferring threat and assessing the impact of those decisions more effectively. The problem of threat identification is complicated by number and types, sometimes unknown, of RF emitters in the littoral environments where the features used for their classification are highly multidimensional, possibly noisy, corrupt, and with large intra-class variations. Due to these input feature characteristics, existing algorithms are ineffective for dealing with complex unreliable and uncertain multi-dimensional multi-source data streams. We propose to confront the challenge of processing these data streams by designing an adaptive context-dependent multi-layer hybrid fusion process engine that combines heuristic and connectionist approaches to feature extraction, selection, and classification

* information listed above is at the time of submission.

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