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Algorithms for Look-down Infrared Target Exploitation

Award Information
Agency: Department of Defense
Branch: National Geospatial-Intelligence Agency
Contract: HM047620C0020
Agency Tracking Number: NGA-P2-20-01
Amount: $999,979.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: NGA18A-001
Solicitation Number: 18.A
Timeline
Solicitation Year: 2018
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-06-01
Award End Date (Contract End Date): 2022-05-31
Small Business Information
P.O. Box 346
Calumet, MI 49913
United States
DUNS: 803724301
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Timothy Havens
 Pricipal Invesigator / Subject Matter Expert
 (906) 487-3115
 thavens@mtu.edu
Business Contact
 Tammy Pini
Phone: (906) 337-3360
Email: tpini@signatureresearchinc.com
Research Institution
 Michigan Technological University
 Ms. Marilyn Haapapuro Ms. Marilyn Haapapuro
 
1400 Townsend Drive
Houghton, MI 49931
United States

 (906) 487-1977
 Nonprofit college or university
Abstract

The multidisciplinary area of GEOINT is changing and becoming more complex. A major driver of innovation in GEOINT collection and processing is artificial intelligence (AI). AI is being leveraged to help accomplish spatial analysis, change detection, and image or video triage tasks where filtering objects of interest from large volumes of data is critical. NGA is confronting the changing GEOINT landscape with emerging data models and development campaigns such as the 2020 Analysis Technology Plan and Strategy 2025. In response to this, we have developed methods to rapidly build datasets consisting of large volumes of physically realistic infrared ground order of battle exemplar images. We have shown that these datasets can be used to train and test machine learning algorithms for recognition of real-world targets.  To further this work, we propose to build a software prototype that can train machine learning algorithms for defense applications by the following: i) procedural generation of large volumes of pertinent infrared imagery, and ii) state-of-the-art machine learning and explainable AI for interpretable target recognition and feature extraction. We will validate this prototype with a culminating experiment to show that radiometrically-accurate synthetic data can be used to train machine learning algorithms for predictable real-world performance.

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

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