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Robust Classification through Deep Learning and Dynamic Multi-Entity Bayesian Reasoning

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0147-16-C-7603
Agency Tracking Number: B15C-001-0077
Amount: $99,992.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: MDA15-T001
Solicitation Number: 2016.0
Solicitation Year: 2016
Award Year: 2016
Award Start Date (Proposal Award Date): 2016-04-25
Award End Date (Contract End Date): 2016-11-24
Small Business Information
20532 El Toro Rd Ste 303
Mission Viejo, CA 92692
United States
DUNS: 825470987
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Rich Wolf
 (949) 716-4290
Business Contact
 Holly Bertrand
Phone: (949) 716-4290
Research Institution
 Oregon State University
 Patricia Hawk
312 Kerr Administration Building
Corvallis, OR 97331
United States

 (541) 737-4933
 Nonprofit College or University

Missile defense faces the challenges of rapidly maturing and evolving complex threats, possessing capabilities which require the use of all available resources to successfully detect, track and identify the lethal objects. Future performance will rely on multiple sensors such as ground and sea based radars and electro-optical and infrared sensors for target recognition. It is crucial to develop a multi-sensor framework capable of processing the wealth of information these sophisticated systems can provide while also accounting for missing, noisy, or corrupted data. ExoAnalytic Solutions has partnered with Oregon State University to develop an advanced multi-sensor, multi-information -driven classifier for robust threat identification. We propose to do this through a unique combination of innovative advances in deep, hierarchical machine learning together with recurrent Deep Learning Neural Network (DLNN) methods for sensor fusion and Dynamic Multi-Entity Bayesian Networks (MEBNs) for whole-scene and contextual reasoning. Approved for Public Release 16-MDA-8620 (1 April 16)

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

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