The surge of deep-space probes makes it unsustainable to navigate them with standard radiometric tracking. Self-driving interplanetary satellites represent a solution to this problem. In this work, a full vision-based navigation algorithm is built by combining an orbit determination method with an image processing pipeline suitable for interplanetary transfers of autonomous platforms. To increase the computational efficiency of the algorithm, a non-dimensional extended Kalman filter is selected as state estimator, fed by the positions of the planets extracted from deep-space images. An enhancement of the estimation accuracy is performed by applying an optimal strategy to select the best pair of planets to track. Moreover, a novel analytical measurement model for deep-space navigation is developed providing a first-order approximation of the light-aberration and light-time effects. Algorithm performance is tested on a high-fidelity, Earth--Mars interplanetary transfer, showing the algorithm applicability for deep-space navigation.

翻译：深空探测器的激增使得依赖标准无线电跟踪技术对其进行导航的方式难以为继。自主飞行的行星际卫星为解决这一问题提供了可能。本文通过将轨道确定方法与适用于自主平台行星际转移的图像处理流程相结合，构建了一套完整的基于视觉的导航算法。为提高算法计算效率，采用无量纲扩展卡尔曼滤波器作为状态估计器，其输入为从深空图像中提取的行星位置。通过应用最优策略选择最佳行星追踪对，进一步提升估计精度。此外，本文提出了一种新颖的深空导航解析测量模型，可对光行差和光行时效应进行一阶近似。算法在地球-火星高保真行星际转移场景中进行了测试，验证了其在深空导航中的适用性。