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Visibility Conflict Resolution for Multiple Antennae and Multi-satellites via Genetic Algorithm

机译:通过遗传算法对多天线和多卫星的可见性冲突解决

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Satellite mission control systems typically are operated by scheduling missions to the visibility between ground stations and satellites. The communication for the mission is achieved by interacting with satellite visibility and ground station support. Specifically, the satellite forms a cone-type visibility passing over a ground station, and the antennas of ground stations support the satellite. When two or more satellites pass by at the same time or consecutively, the satellites may generate a visibility conflict. As the number of satellites increases, solving visibility conflict becomes important issue. In this study, we propose a visibility conflict resolution algorithm of multi-satellites by using a genetic algorithm (GA). The problem is converted to scheduling optimization modeling. The visibility of satellites and the supports of antennas are considered as tasks and resources individually. The visibility of satellites is allocated to the total support time of antennas as much as possible for users to obtain the maximum benefit. We focus on a genetic algorithm approach because the problem is complex and not defined explicitly. The genetic algorithm can be applied to such a complex model since it only needs an objective function and can approach a global optimum. However, the mathematical proof of global optimality for the genetic algorithm is very challenging. Therefore, we apply a greedy algorithm and show that our genetic approach is reasonable by comparing with the performance of greedy algorithm application.
机译:卫星代表控制系统通常通过将任务调度到地面站和卫星之间的可见性来操作。通过与卫星可视性和地面支持的互动来实现任务的沟通。具体地,卫星形成通过地面站的锥形能见度,地面站的天线支撑卫星。当两个或多个卫星通过同时或连续通过时,卫星可能会产生可见性冲突。随着卫星的数量增加,解决能见度冲突变得重要问题。在这项研究中,我们通过使用遗传算法(GA)提出了多卫星的可见性冲突解决算法。问题被转换为调度优化建模。卫星的可见性和天线的支持被认为是单独的任务和资源。尽可能多地分配卫星的可见性,以便用户获得最大效益的天线的总支持时间。我们专注于遗传算法方法,因为问题很复杂,没有明确定义。遗传算法可以应用于这种复杂模型,因为它只需要目标函数并且可以接近全局最优。然而,遗传算法全球最优性的数学证据非常具有挑战性。因此,我们应用了一种贪婪的算法,并表明我们的遗传方法是合理的,与贪婪算法应用的性能相比。

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