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Co-Orbital Threat Prediction and Assessment

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8750-23-C-0506
Agency Tracking Number: F22B-T001-0035
Amount: $149,986.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: SF22B-T001
Solicitation Number: 22.B
Timeline
Solicitation Year: 2022
Award Year: 2023
Award Start Date (Proposal Award Date): 2022-12-13
Award End Date (Contract End Date): 2023-09-14
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
 Bradyn Morton
 (505) 244-1222
 bradyn.morton@tautechnologies.com
Business Contact
 Bob Kim
Phone: (505) 244-1222
Email: bob.kim@tautechnologies.com
Research Institution
 Texas A&M University
 Kyle DeMars
 
741C HRBB
College Station, TX 77843-3141
United States

 (979) 845-1640
 Federally Funded R&D Center (FFRDC)
Abstract

The modern space environment is dynamic and contested, requiring constant surveillance to detect maneuvers and thwart malicious intents. Modern maneuver detection techniques rely on either increasing solution noise or estimating maneuvers by propagating multiple models. Both suffer during long data gaps or in cluttered environments, thereby yielding inconsistent results in proliferated orbital regimes. By combining Random Finite Set theory and Joint Input State Estimation, a hybrid Bernoulli filter will be built to probabilistically detect anomalies in spacecraft tracks using heterogeneous sensor data and determine if the tracks vary from the long-term mean. This will increase accuracy, reduce computational load, and allow for faster threat detection. It is unrealistic to predict all possible future behaviors of every satellite on orbit in real time so a threat model is required to probabilistically predict such outcomes. This will give a statistical likelihood of threats based on a set of parameters. During this Phase I, Tau and Texas A&M will develop this hybrid filter and design a threat posture model to accurately predict future malicious actions.

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

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