A Markov Game Theoretic High Level Data Fusion Approach for Defensive Counterspace

We propose a highly innovative Markov (stochastic) game theoretic level-3 data fusion approach for defensive counterspace. It is known that Bayesian Network is an insightful approach to determine optimal strategies against adversarial opponent. However, it lacks the essential adversarial decision processes perspective and has the following disadvantages: 1) it needs prior information (or details specific to an unidentified adversary), and 2) assumes only one player. Since game theory is more realistic for addressing the presence of intelligent adversary in decision making, Markov (Stochastic) game model is used to estimate the belief of each possible enemy COA (ECOA), which is proposed here to represent the effect of satellite threats such as space weather and man-made threats. Our multiplayer non-zero sum game theoretic approach is more effective because it takes into account the fact that both the adversary and the neutral players (normal spacecrafts) are intelligent. We integrate the deception concept in our game approach to model the action of purposely rendering partial information to hide the space attackers. With the consideration that an attacker may act like a neutral or white object (normal spacecrafts), we also model the actions of white objects in our non-zero sum Markov game framework.