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Sparse Information Orbit Estimation for Proliferated LEO

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: W31P4Q-21-C-0032
Agency Tracking Number: D2-2568
Amount: $1,472,588.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: HR001119S0035-22
Solicitation Number: 19.A22
Solicitation Year: 2019
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-03-18
Award End Date (Contract End Date): 2022-06-20
Small Business Information
PO Box 9334
Albuquerque, NM 87119-1111
United States
DUNS: 176086952
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Edwin Pease
 (505) 244-1222
Business Contact
 Bob Kim
Phone: (505) 244-1222
Research Institution
 The University of Texas at Austin Office of Sponsored Projects
 Renato Zanetti
3925 W. Braker Lane, Suite 3.11072 (Mail Code A9000)
Austin, TX 78759-8949
United States

 (512) 471-5145
 Nonprofit College or University

The current Space Surveillance Network (SSN) is projected to soon be unable to track all manmade Low Earth Orbit (LEO) objects. Unprecedentedly large constellations of satellites will exponentially grow the number of LEO objects, and simply adding more sensors to the SSN to keep pace with the proliferation in LEO is a cost-prohibitive proposition. This research proposes to address the proliferation of LEO constellations/objects using novel estimation algorithms without the need to expand or improve the current SSN configuration. In particular, the work focuses a novel estimation algorithm with nearly linear-time complexity that incorporates multi-fidelity, GPU-based orbit propagation, a hybrid Gaussian mixture sampled particle filter with adaptive domain partitioning, and an adaptive, statistically optimal multi-hypothesis filter that can handle massive constellations, nearly spaced objects, and slowly maneuvering satellites.

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

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