Recent advances in Artificial Intelligence (AI) and Cyber-Physical Systems (CPS) for aerospace applications have brought about new opportunities for the fast-growing satellite industry. The progressive introduction of connected satellite systems and associated mission concepts is stimulating the development of intelligent CPS (iCPS) architectures, which can support high levels of flexibility and resilience in an increasingly congested near-Earth space environment. The need for higher levels of automation and autonomy in satellite operations has stimulated numerous research initiatives in recent years, focusing on the progressive enhancement of systemic performance (e.g., addressing safety, integrity and cyber-physical security metrics) and associated monitoring/augmentation approaches that can support Trusted Autonomous Satellite Operations (TASO). Despite these advances, in most contemporary satellite platforms, autonomy is restricted to a specific set of rules and cases, while the transition to TASO requires a paradigm shift in the design of both space vehicles and ground-based systems. In particular, the use of AI is seen as an essential enabler for TASO as it enhances system performance/adaptability and supports both predictive and reactive integrity augmentation capabilities, especially in Distributed Satellite Systems (DSS). This article provides a critical review of AI for satellite operations, with a special focus on current and likely future DSS architectures for communication, navigation and remote sensing missions. The aim is to identify key contemporary challenges and opportunities associated with space iCPS design methodologies to enhance the performance and resilience of satellite systems, supporting the progressive transition to TASO. A comprehensive review of relevant AI techniques is presented to critically assess the potential benefits and challenges of each method for different space applications. After describing the specificities of DSS and the opportunities offered by iCPS architectures, the co-evolution of space and control (ground and on-board) segments is highlighted as an essential next step towards enabling TASO. As an integral part of this evolutionary approach, the most important legal and regulatory challenges associated with the adoption of AI in TASO are also discussed.

航空航天应用人工智能（AI）和网络物理系统（CPS）的最新进展为快速发展的卫星行业带来了新的机遇。互联卫星系统和相关任务概念的逐步引入正在刺激智能 CPS (iCPS) 架构的发展，该架构可以在日益拥挤的近地空间环境中支持高水平的灵活性和弹性。近年来，卫星运行对更高水平的自动化和自主性的需求刺激了许多研究举措，重点关注系统性能的逐步增强（例如，解决安全性、完整性和网络物理安全指标）以及相关的监控/增强方法，可以支持可信自主卫星操作（TASO）。尽管取得了这些进步，但在大多数当代卫星平台中，自主性仅限于一组特定的规则和案例，而向 TASO 的过渡需要航天器和地面系统设计的范式转变。特别是，人工智能的使用被视为 TASO 的重要推动者，因为它增强了系统性能/适应性，并支持预测性和反应性完整性增强功能，特别是在分布式卫星系统 (DSS) 中。本文对卫星操作中的人工智能进行了批判性评论，特别关注当前和未来可能的通信、导航和遥感任务的 DSS 架构。其目的是确定与空间 iCPS 设计方法相关的当代主要挑战和机遇，以增强卫星系统的性能和弹性，支持逐步过渡到 TASO。对相关人工智能技术进行了全面回顾，以批判性地评估每种方法对于不同空间应用的潜在好处和挑战。在描述了 DSS 的特殊性和 iCPS 架构提供的机会之后，空间和控制（地面和机载）部分的共同演化被强调为实现 TASO 的重要的下一步。作为这种演进方法的一个组成部分，还讨论了与 TASO 采用人工智能相关的最重要的法律和监管挑战。