We develop a Gravity Assist Mapping to quantify the effects of a flyby in a two-dimensional circular restricted three-body situation based on Gaussian Process Regression (GPR). This work is inspired by the Keplerian Map and Flyby Map. The flyby is allowed to occur anywhere above 300 km altitude at the Earth in the system of Sun-(Earth+Moon)-spacecraft, whereas the Keplerian map is typically restricted to the cases outside the Hill sphere only. The performance of the GPR model and the influence of training samples (number and distribution) on the quality of the prediction of post-flyby orbital states are investigated. The information provided by this training set is used to optimize the hyper-parameters in the GPR model. The trained model can make predictions of the post-flyby state of an object with an arbitrary initial condition and is demonstrated to be efficient and accurate when evaluated against the results of numerical integration. The method can be attractive for space mission design.

我们开发了重力辅助贴图，以基于高斯过程回归（GPR）在二维圆形受限三体情况下量化飞掠的影响。这项工作的灵感来自开普勒地图和Flyby地图。飞越被允许在太阳（地球+月球）飞船系统中地球上海拔300公里以上的任何地方发生，而开普勒地图通常仅限于希尔球体以外的情况。研究了GPR模型的性能以及训练样本（数量和分布）对飞行后轨道状态预测质量的影响。该训练集提供的信息用于优化GPR模型中的超参数。经过训练的模型可以对具有任意初始条件的物体的飞越状态做出预测，并且在针对数值积分结果进行评估时，该模型被证明是有效且准确的。该方法对于太空任务设计可能具有吸引力。