An escape strategy in orbital pursuit-evasion games with incomplete information

Abstract

The orbital pursuit-evasion game is typically formulated as a complete-information game, which assumes the payoff functions of the two players are common knowledge. However, realistic pursuit-evasion games typically have incomplete information, in which the lack of payoff information limits the player’s ability to play optimally. To address this problem, this paper proposes a currently optimal escape strategy based on estimation for the evader. In this strategy, the currently optimal evasive controls are first derived based on the evader’s guess of the pursuer’s payoff weightings. Then an online parameter estimation method based on a modified strong tracking unscented Kalman filter is employed to modify the guess and update the strategy during the game. As the estimation becomes accurate, the currently optimal strategy gets closer to the actually optimal strategy. Simulation results show the proposed strategy can achieve optimal evasive controls progressively and the evader’s payoff of the strategy is lower than that of the zero-sum escape strategy. Meanwhile, the proposed strategy is also effective in the case where the pursuer changes its payoff function halfway during the game.