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Algorithms for Look-down Infrared Target Exploitation

Award Information
Agency: Department of Defense
Branch: National Geospatial-Intelligence Agency
Contract: HM047618C0063
Agency Tracking Number: NGA-P1-18A-02
Amount: $99,998.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: 1
Solicitation Number: 2018.0
Timeline
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-09-17
Award End Date (Contract End Date): 2019-06-15
Small Business Information
P.O. Box 346, Calumet, MI, 49913
DUNS: 803724301
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Timothy Havens
 (906) 487-3115
 thavens@mtu.edu
Business Contact
 Chris Downs
Phone: (906) 337-3360
Email: downs@signatureresearchinc.com
Research Institution
 Michigan Technological University
 Ms. Marilyn Haapapuro
 1400 Townsend Drive
Houghton, MI, 49931
 (906) 487-1977
 Domestic nonprofit research organization
Abstract
Signature Research, Inc. (SGR) and Michigan Technological University (MTU) propose a Phase I STTR effort to develop a learning algorithm which exploits the spatio-spectral characteristics inherent within IR imagery and motion imagery.Our archive of modelled and labeled data sets will allow our team to thoroughly capture the variable elements that will drive machine learning performance.The overall effort will determine the feasibility of using a learning algorithm to augment the GEOINT professionals analysis of IR look-down order of battle analysis.The specific objectives of the effort include establishing what makes an IR image suitable for use in terms of machine learning recognition performance and determining whether the application of machine learning data processing toward IR image recognition is appropriate for further testing.It is critical to have a diverse training data set for any machine learning algorithm to exploit and process IR imagery due to the wide variety of target environments.A robust algorithm using a cross-validation approach for data-derived model learning will be created by augmenting existing IR imagery sets with simulated thermal scenes, physics-based rendering, atmospheric, and empirical sensor models, allowing the algorithm to exploit targets in a wide variety of conditions.

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

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