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.
Dan Shen, Carolyn Sheaff, Jingyang Lu, Genshe Chen, Erik Blasch, and Khanh Pham, "Adaptive markov inference game optimization (AMIGO) for rapid Discovery of satellite behaviors," Proc. SPIE 11017, Sensors and Systems for Space Applications XII, 1101708 (Presented at SPIE Defense + Commercial Sensing: April 15, 2019; Published: 29 July 2019); https://doi.org/10.1117/12.2520755.
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