Fusion in a Cloud

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-14-P-1091
Agency Tracking Number: N132-135-0079
Amount: $79,925.00
Phase: Phase I
Program: SBIR
Awards Year: 2014
Solitcitation Year: 2013
Solitcitation Topic Code: N132-135
Solitcitation Number: 2013.2
Small Business Information
Black River Systems Company, Inc.
162 Genesee Street, Utica, NY, -
Duns: 111305843
Hubzone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Peter Shea
 Principal Investigator
 (315) 732-7385
Business Contact
 Milissa Benincasa
Title: Vice President
Phone: (315) 732-7385
Email: benincasa@brsc.com
Research Institution
One of the major benefits of tactical cloud computing is improved net-centric capabilities and operations with the objective of information dominance. The reassign-able pools of computing resources and efficient information across operational boundaries can produce a more accurate and up to date common operational picture for warfighters. Instead of individual platforms pulling data from sensors to fuse and analyze locally for battle space and situational awareness, warfighters will be able to increase the accuracy of local scenes through remote data processing and sharing. Black River Systems Company will leverage its existing Distributed Fusion Manager (DFM) for deployment of distributed Level 1 and 2 fusion algorithms to the cloud. The DFM currently performs distributed Level 1 fusion by synchronizing track identities and performing track to track fusion over a network of tracking platforms. The DFM will be enhanced to provide an infrastructure for Level 2 fusion that will combine both probabilistic and non-probabilistic learning and inferencing models across geographically separated nodes. At the conclusion of Phase I, Level 1 fusion algorithms will be demonstrated using simulated Surface-Moving-Target-Indicator (SMTI) and radar tracks; Level 2 fusion algorithms will be demonstrated using relevance vector machine, support vector machine, and Bayesian network models.

* information listed above is at the time of submission.

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