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Sparse Information Orbit Estimation for Proliferated LEO

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: 140D0420C0062
Agency Tracking Number: DHR001119S0035-22-0005
Amount: $222,567.00
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): 2020-03-22
Award End Date (Contract End Date): 2020-12-22
Small Business Information
PO Box 9334
Albuquerque, NM 87119
United States
DUNS: 176086952
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Edwin Pease
 Principal Investigator
 (505) 244-1222
Business Contact
 JJ Kelly
Phone: (505) 244-1222
Research Institution
 Elena Mota
 Elena Mota Elena Mota
3925 West Braker Lane Suite 3.340 - Mail Stop A9000
Austin, TX 78759
United States

 (512) 471-6424
 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 at the current rate of observation. Very 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 orbit propagation, a combination particle and Gaussian sum filter update, and built-in, statistically optimal data association.

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

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