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Accelerated LADAR Exploitation

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-19-C-6054
Agency Tracking Number: F103-042-1889a
Amount: $949,991.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: AF103-042
Solicitation Number: 10.3
Solicitation Year: 2010
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-05-17
Award End Date (Contract End Date): 2021-05-17
Small Business Information
714 East Monument Avenue Suite 201
Dayton, OH 45402
United States
DUNS: 601628717
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Scott Cone
 Senior Research Scientist
 (937) 974-1683
Business Contact
 Michael McCloskey
Phone: (937) 684-3339
Research Institution

The demand for 3D LADAR imaging is growing both in the DoD and commercial sectors. However, exploitation processes and tools have lagged advances in LADAR technologies. In a prior Phase II SBIR effort, 361 Interactive leveraged a Cognitive Systems Engineering approach to develop a LADAR analysis tool that was implemented as a Quick Terrain Modeler plug-in. The tool consisted of automated feature extraction and machine learning-based object classification algorithms along with a novel user interface for interacting with the algorithms and their output. The current effort will build upon that prototype product to expand and enhance both the algorithm and UI capabilities. Our overall objective for this Phase II follow-on effort is to develop a mission-driven, analyst-centric LADAR analysis capability that fosters human-machine teaming to improve the accuracy, efficiency and effectiveness of the LADAR exploitation workflow. We will employ a user-centric approach involving frequent and substantive interaction with end users, and incorporation of state-of-the-art machine learning algorithms to segment and classify LADAR data sets. The final product will seamlessly integrate into the analyst’s workflow, provide much better accuracy, and significantly expand the range of classes and subclasses that can be detected.

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

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