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Sparse Information Orbit Estimation for Proliferated LEO

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: 140D0420C0055
Agency Tracking Number: DHR001119S0035-22-0006
Amount: $224,976.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: HR001119S0035-22
Solicitation Number: DARPA HR001119S0035-22
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-02-24
Award End Date (Contract End Date): 2020-11-24
Small Business Information
559 E. Pikes Peak Ave. Suite 300
Colorado Springs, CO 80903
United States
DUNS: 623964439
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Joe Powell
 Astrodynamic Lead
 (719) 351-8015
Business Contact
 Heidi Wright
Phone: (334) 590-4202
Research Institution
 University of Colorado at Boulder
 Laura L. Burfield Laura L. Burfield
1800 Grant Street, Suite 600
Denver, CO 80203
United States

 (303) 492-6646
 Nonprofit College or University

Rapidly expanding Low Earth Orbit (LEO) satellite constellations force traditional ground-based tracking methods to adapt as the current ground sensor networks can no longer provide a data rich tracking environment. Accurate tracking information is continually consumed by the Government and private sector to varying degrees of accuracy throughout satellites’ mission lifecycles to provide and utilize services like earth-imaging, weather imaging, situational awareness, telecom, media distribution, and internet service provision. In cooperation with the University of Colorado at Boulder, Braxton overcomes the sparse tracking data problem by utilizing modern statistical and machine learning techniques to maintain adequate orbit estimation for large numbers of spacecraft. A single orbit estimation algorithm is unsuitable for addressing a multitude of situations including CubeSat swarm launches, orbit colocation issues, and object break-up scenarios; therefore, our solution provides a plug and play architecture in which multiple orbit estimation techniques process an object’s tracking observations and the optimal estimation technique is automatically identified and critiqued against varying criteria such as accuracy, processing requirements, and solution maintainability.

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

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