The orbital pursuit-evasion game (OPE) is a topic of research that has been attracting increasing attention from scholars. However, most works based on the relative dynamics under a short-distance assumption is not applicable when the distance between two spacecrafts is too large. Accordingly, there should be two phases in the OPE, a long-distance OPE (LDOPE) as well as a short-distance one. This article concerns on the optimal guidance problem for the LDOPE. Two different models are introduced in this article to formulate the LDOPE, namely, the Cartesian model, and the spherical model. Then, to overcome the unacceptable solution computation time of traditional algorithms, such as the differential evolution (DE), a well-designed algorithm called “mixed global-local optimization strategy” (MGLOS), which consists of the global optimization phase, and the local optimization phase, is introduced in this article. The MGLOS is nearly two orders of magnitude more efficient than the DE. Moreover, simulations under different initial conditions demonstrate the robustness of the algorithm, and the accuracy, and efficiency of the Cartesian, and spherical models, respectively. Finally, the robustness of two models is analyzed by Monte Carlo simulation, which provides a quantified way to make a choice between two models depending on the measurement accuracy, and permitted maximum error.

中文翻译：

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远程轨道逃避游戏的两种最优制导方法的比较

轨道逃避游戏（OPE）是一个研究主题，受到了越来越多学者的关注。但是，当两个航天器之间的距离太大时，大多数基于短距离假设下的相对动力学的工作都不适用。因此，在OPE中应该有两个阶段，即长距离OPE（LDOPE）和短距离OPE。本文关注LDOPE的最佳制导问题。本文介绍了两种不同的模型来表示LDOPE，即笛卡尔模型和球面模型。然后，为了克服传统算法（如差分进化（DE））的不可接受的解决方案计算时间，精心设计的算法称为“混合全局-局部优化策略”（MGLOS），它由全局优化阶段组成，本文介绍了局部优化阶段。MGLOS的效率比DE高近两个数量级。此外，在不同初始条件下的仿真分别证明了该算法的鲁棒性，笛卡尔和球形模型的准确性和效率。最后，通过蒙特卡洛模拟分析了两个模型的鲁棒性，这提供了一种量化方法，可以根据测量精度和允许的最大误差在两个模型之间进行选择。分别。最后，通过蒙特卡洛模拟分析了两个模型的鲁棒性，这提供了一种量化方法，可以根据测量精度和允许的最大误差在两个模型之间进行选择。分别。最后，通过蒙特卡洛模拟分析了两个模型的鲁棒性，这提供了一种量化方法，可以根据测量精度和允许的最大误差在两个模型之间进行选择。