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Target Recognition and Acquisition in Complex Environments (TRACE)

Award Information
Agency: Department of Defense
Branch: Space Development Agency
Contract: HQ0850-21-P-0004
Agency Tracking Number: SDA21D-08-0022
Amount: $250,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: HQ085021S0001-08
Solicitation Number: HQ085021S0001.D
Timeline
Solicitation Year: 2021
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-09-30
Award End Date (Contract End Date): 2022-09-30
Small Business Information
6800 Cortona Drive
Goleta, CA 93117-3021
United States
DUNS: 054672662
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Christopher Agh
 (805) 968-6787
 cagh@toyon.com
Business Contact
 Marcella Lindbery
Phone: (805) 968-6787
Email: mlindbery@toyon.com
Research Institution
 Air Force Institute of Technology (AFIT)
 Lt. Col. Michael Dexter
 
2950 Hobson Way
Wright-Patterson AFB, OH 45433-0000
United States

 (321) 591-9808
 Nonprofit College or University
Abstract

The Space Development Agency’s (SDA) Tracking Layer is tasked with providing global indications, warning, tracking, and targeting of advanced missile threats, including hypersonic missile systems. They are currently leading the development of an Overhead Persistent Infrared (OPIR) Missile Tracking satellite constellation in LEO as part of the DoD’s future threat-driven National Defense Space Architecture (NDSA). Satellite imagery from sensors in LEO typically contain complex cluttered backgrounds accentuated by non-stationary platform motion making it even more challenging to discern new threats of interest. On this effort, Toyon Research Corporation and the Air Force Institute of Technology (AFIT) propose to leverage the AFIT Scene and Sensor Emulation Tool (ASSET) for modeling sensors in LEO and simulating relevant data for algorithm development and testing. Algorithm development will entail methods using novel vision-based geometry for scene registration and parallax mitigation necessary for clutter suppression in data from non-stationary sensors. We will also develop algorithms for automated recognition and acquisition of new threat scenarios leveraging track-before-detect and deep learning architectures. Lastly, algorithms will be optimized to enable integration with future operational systems including on-orbit processing hardware.

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

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