There is no doubt that the Moon has become the center of interest for commercial and international actors. Over the past decade, the number of planned long-term missions has increased dramatically. This makes the establishment of cislunar space networks (CSNs) crucial to orchestrate uninterrupted communications between the Moon and Earth. However, there are numerous challenges, unknowns, and uncertainties associated with cislunar communications that may pose various risks to lunar missions. In this study, we aim to address these challenges for cislunar communications by proposing a machine learning-based cislunar space domain awareness (SDA) capability that enables robust and secure communications. To this end, we first propose a detailed channel model for selected cislunar scenarios. Secondly, we propose two types of interference that could model anomalies that occur in cislunar space and are so far known only to a limited extent. Finally, we discuss our cislunar SDA to work in conjunction with the spacecraft communication system. Our proposed cislunar SDA, involving heuristic learning capabilities with machine learning algorithms, detects interference models with over 96% accuracy. The results demonstrate the promising performance of our cislunar SDA approach for secure and robust cislunar communication.

翻译：毋庸置疑，月球已成为商业和国际行为体的关注焦点。过去十年间，计划中的长期任务数量急剧增加。这使得建立月球空间网络（CSNs）对于协调月球与地球之间的不间断通信至关重要。然而，月球通信面临诸多挑战、未知因素和不确定性，可能对月球任务构成各类风险。本研究旨在通过提出一种基于机器学习的月球空间领域感知（SDA）能力来应对这些挑战，从而实现稳健且安全的通信。为此，我们首先针对选定的月球场景提出详细的信道模型。其次，我们提出两种干扰类型，以模拟月球空间中出现但迄今仅有限已知的异常现象。最后，我们讨论如何将月球空间领域感知与航天器通信系统协同工作。我们提出的月球空间领域感知方案结合了基于机器学习算法的启发式学习能力，能以超过96%的准确率检测干扰模型。结果表明，我们提出的月球空间领域感知方法在实现安全稳健的月球通信方面具有良好性能。