Collaborative Anti-Submarine Warfare (ASW) Threat Assessment

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$80,000.00
Award Year:
2011
Program:
SBIR
Phase:
Phase I
Contract:
N00167-11-P-0042
Award Id:
n/a
Agency Tracking Number:
N102-154-0801
Solicitation Year:
2010
Solicitation Topic Code:
N102-154
Solicitation Number:
2010.2
Small Business Information
559 West Uwchlan Avenue, Suite 140, Exton, PA, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
075485425
Principal Investigator:
C. Butler
President
(757) 727-7700
Allen.Butler@va.wagner.com
Business Contact:
W. Monach
Vice President
(757) 727-7700
GovtMktg@pa.wagner.com
Research Institution:
Stub




Abstract
In this project, Wagner Associates, with George Mason University and DDL OMNI as subcontractors, will develop the ASW Threat Prioritization System (ATPS). This system will: I. Prioritize targets based on threat potential, combining Direct Classification Evidence (e.g. feature measurement clues) and Indirect Classification Evidence (e.g. tactical events and relationship clues). II. Reduce the time to make threat contact engagement decisions. In order to achieve Goal I, we will combine the classification/identification capabilities of Wagner"s Bayesian Inference Engine with the inter-entity reasoning capabilities of GMU"s Multi-Entity Bayesian Networks and Probabilistic Ontologies. This Level 2/3 Data Fusion functionality will be built on top of the existing Level 1 Fusion algorithms (agent-based simulation, Bayesian statistical, and non-Gaussian optimization) currently implemented in the Undersea Warfare Decision Support System (USW-DSS) Data Fusion Engine (DFEN) and Mission Optimization Configuration Item (MOCI). Achieving Goal II requires extracting the knowledge within ATPS and presenting it to the operator in a way that improves his performance. It is critical that the operator be able to"see"into the ATPS reasoning. The criteria used by the system for promoting contacts to a"threat warning"level will require careful development to avoid a system with excessive false alarms.

* information listed above is at the time of submission.

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