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Use of Augmented Reality in Experimentation with New Equipment Training for Electro-Optic Infrared (EOIR) Sensors

Award Information
Agency: Department of Defense
Branch: Army
Contract: W909MY-16-C-0021
Agency Tracking Number: A161-041-0660
Amount: $100,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: A16-041
Solicitation Number: 2016.1
Timeline
Solicitation Year: 2016
Award Year: 2016
Award Start Date (Proposal Award Date): 2016-07-29
Award End Date (Contract End Date): 2017-01-28
Small Business Information
6800 Cortona Drive
Goleta, CA 93117
United States
DUNS: 054672662
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 John Berger, Ph.D.
 (805) 968-6787
 jberger@toyon.com
Business Contact
 Ms. Marcella Lindbery
Phone: (805) 968-6787
Email: mlindbery@toyon.com
Research Institution
N/A
Abstract

Training the warfighter to employ new electro-optic infrared (EOIR) sensing technologies for target acquisition requires a realistic representation of operational challenges including threat target profiles, environmental clutter, and sensor engineering limitations. Augmented reality (AR) enables the efficient training by allowing the Soldier to directly focus on the most important items within an EOIR sensor feed. Toyon Research Corporation proposes to develop an experimentation capability for training the target acquisition process by integrating a real-time image processing technology to augment video, a geospatial representation of targets, landmarks, and other geo-referenced items, and a learning management system to enable higher level reasoning in the context of various missions and scenarios. In Phase I, Toyon will develop psychophysical experimentation architectures to identify the most critical training components to vary and stimulate in a live training environment. Toyon will develop a methodology for evaluating the responses to effectively train the Soldier for rapidly acquiring targets. Geospatial data will be displayed on the EOIR feed by using high-fidelity sensor models for estimating target locations. As a result, Toyons proposed approach is display agnostic and will embed on screen display data on existing real-time EOIR monitors.

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

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