An escape strategy in orbital pursuit-evasion games with incomplete information
An escape strategy in orbital pursuit-evasion games with incomplete information
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DOI：
10.1007/s11431-020-1662-0
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
Zhen-yu Li;Hai Zhu;Ya-zhong Luo
中科院分区：
文献类型：
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作者：
Zhen-yu Li;Hai Zhu;Ya-zhong Luo
轨道追逃博弈通常被表述为完全信息博弈，该博弈假设双方的收益函数为常识。然而，现实的追捕-逃避游戏通常具有不完全信息，其中缺乏收益信息限制了玩家的最佳体验能力。为了解决这一问题，本文提出了一种基于估计的当前最优逃避策略。在该策略中，首先根据逃避者对追踪者收益权重的猜测推导出当前最优规避控制。然后采用一种基于改进的强跟踪无嗅卡尔曼滤波的在线参数估计方法对猜测结果进行修正，并在博弈过程中更新策略。随着估计越来越精确，当前最优策略越来越接近实际最优策略。仿真结果表明，该策略能逐步达到最优规避控制，且规避者的收益低于零和规避策略。同时，该策略在追捕者中途改变其支付函数的情况下也是有效的。
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.