A new era of space exploration and exploitation is fast approaching. A multitude of spacecraft will flow in the future decades under the propulsive momentum of the new space economy. Yet, the flourishing proliferation of deep-space assets will make it unsustainable to pilot them from ground with standard radiometric tracking. The adoption of autonomous navigation alternatives is crucial to overcoming these limitations. Among these, optical navigation is an affordable and fully ground-independent approach. Probes can triangulate their position by observing visible beacons, e.g., planets or asteroids, by acquiring their line-of-sight in deep space. To do so, developing efficient and robust image processing algorithms providing information to navigation filters is a necessary action. This paper proposes an innovative pipeline for unresolved beacon recognition and line-of-sight extraction from images for autonomous interplanetary navigation. The developed algorithm exploits the k-vector method for the non-stellar object identification and statistical likelihood to detect whether any beacon projection is visible in the image. Statistical results show that the accuracy in detecting the planet position projection is independent of the spacecraft position uncertainty. Whereas, the planet detection success rate is higher than 95% when the spacecraft position is known with a 3sigma accuracy up to 10^5 km.

翻译：太空探索与利用的新时代正迅速来临。在新太空经济的推动下，未来几十年将有大量航天器升空。然而，深空资产的蓬勃增长将使得使用标准无线电跟踪技术从地面操控这些航天器变得不可持续。采用自主导航替代方案对于克服这些限制至关重要。其中，光学导航是一种经济且完全独立于地面的方法。探测器可通过观测可见信标（例如行星或小行星），获取其在深空中的视线方向，进而三角测量自身位置。为此，开发高效且鲁棒的图像处理算法以向导航滤波器提供信息，是一项必要的工作。本文提出了一种创新流水线，用于从图像中识别未解析信标并提取视线方向，以实现自主星际导航。所开发的算法利用k-vector方法进行非恒星目标识别，并基于统计似然性检测图像中是否有任何信标投影可见。统计结果表明，检测行星位置投影的精度与航天器位置不确定性无关。而当航天器位置以3σ精度已知、误差范围高达10^5公里时，行星探测成功率超过95%。