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

Space superiority requires space protection and space situational awareness (SSA), which rely on rapid and accurate space object behavioral and operational intent discovery. The focus of this project is to develop a stochastic approach for rapid discovery of evasive satellite behaviors. Designing the innovative decision support tool has numerous challenges: (i) partial observable actions; (ii) evasive resident space objects; (iii) uncertainties modeling and propagation; (iv) real-time requirement and computational intractable algorithms.We propose a solution called Adaptive Markov Inference Game Optimization (AMIGO) for rapid discovery of evasive satellite behaviors. AMIGO is an adaptive feedback game theoretic approach. It gets information through sensors about the relations between the RSOs and ground/space surveillance assets (SAs). The relations are determined by both the RSOs and SAs. Therefore, it is a game instead of control problem. The game reasoning interacts with data level fusion, the uncertainty modeling/propagation, and RSO detection/tracking to predict the future RSOs-SAs relations. The game engine also supports optional space pattern dictionary/semantic rules for adaptive transition matrices in our Markov game. The outputs of our game reasoning include two kinds of controls: one for SAs and the other for RSOs. The two sets form a game equilibrium.