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Proceedings Paper

Adaptive markov inference game optimization (AMIGO) for rapid Discovery of satellite behaviors
Author(s): Dan Shen; Carolyn Sheaff; Jingyang Lu; Genshe Chen; Erik Blasch; Khanh Pham
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Paper Abstract

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 spacebased surveillance assets. This paper develops and implements a solution called Adaptive Markov Inference Game Optimization (AMIGO) for rapid discovery of satellite behaviors. AMIGO is an adaptive feedback game theoretic approach. AMIGO gets information from sensors about the relations between the resident space objects (RSOs) of interest and ground and space surveillance assets (GSAs). The relations are determined by both the RSOs and GSAs. Therefore, AMIGO represents the situation as a game instead of a control problem. The game reasoning utilizes data level fusion, stochastic modeling/propagation, and RSO detection/tracking to predict the future RSOs-GSAs relations. The game engine also supports optional space pattern dictionary/semantic rules for adaptive transition matrices in the Markov game. If no existing pattern dictionary is available, AMIGO builds an initial one and revises it during the game reasoning. The outputs of the AMIGO reasoning include two kinds of control methods: processing of GSA measurements and localization of RSOs. The two sets form a game equilibrium, one for surveillance asset management and the other for the estimation of RSO behaviors. Numerical simulations and visualizations demonstrate the performance of AMIGO.

Paper Details

Date Published: 29 July 2019
PDF
Proc. SPIE 11017, Sensors and Systems for Space Applications XII, 1101708 (29 July 2019); doi: 10.1117/12.2520755
Show Author Affiliations
Dan Shen, Intelligent Fusion Technology, Inc. (United States)
Carolyn Sheaff, Air Force Research Lab. (United States)
Jingyang Lu, Intelligent Fusion Technology, Inc. (United States)
Genshe Chen, Intelligent Fusion Technology, Inc. (United States)
Erik Blasch, Air Force Office of Scientific Research (United States)
Khanh Pham, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 11017:
Sensors and Systems for Space Applications XII
Genshe Chen; Khanh D. Pham, Editor(s)

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