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Adaptive Markov Inference Game Optimization (AMIGO) for Rapid Discovery of Evasive Satellite Behaviors

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8750-19-C-1000
Agency Tracking Number: F17C-T02-0039
Amount: $750,000.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: AF17-CT02
Solicitation Number: 17.C
Solicitation Year: 2017
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-09-27
Award End Date (Contract End Date): 2021-09-27
Small Business Information
20271 Goldenrod Lane Suite 2066
Germantown, MD 20876
United States
DUNS: 967349668
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Dan Shen
 Chief Scientist
 (301) 515-7261
Business Contact
 Yingli Wu
Phone: (949) 596-0057
Research Institution
 Texas A&M University
 Raktim Bhattacharya Raktim Bhattacharya
7607 Eastmark Drive
College Station, MD 77840
United States

 (979) 862-2914
 Nonprofit College or University

Space superiority requires space protection and space situational awareness (SSA), which rely on rapid and accurate space object behavioral and operational intent discovery. The presence of adversaries in addition to real-time and hidden information constraints greatly complicates the decision-making process in controlling both ground-based and space-based Air Force surveillance assets. The focus of this project is to develop robust and near real-time algorithms that rapidly discover the behavioral patterns and operational intent of potentially evasive and/or ambiguous active resident space objects (RSOs). The problem of behaviorally evasive intent identification is challenging for several reasons: (i) partial observable actions; (ii) evasive resident space objects; (iii) uncertainties modeling and propagation; and (iv) real-time requirement and computational intractable algorithms. In phase I, we have developed a game theory enabled machine learning solution. We have tested the solution on Lockheed Martin’s Space Fence dataset and obtained supportive and promising results. In the Phase II, we will refine and expand the Phase I technologies to unify game theory, uncertainties propagation, and machine learning for the rapid discovery of evasive satellite behaviors. We will evaluate the performance of the developed executable prototypical system by using realistic metrics, real-world data (Lockheed Martin Space Fence), and operational constraints.

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

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