Architectures and Synchronization Techniques for Distributed Satellite Systems: A Survey

TL;DR

This survey reviews synchronization techniques for distributed satellite systems, emphasizing architectures, methods, applications, and emerging data-driven approaches, highlighting challenges and future research directions.

Contribution

It categorizes existing synchronization methods, discusses related operations like ranging, and explores emerging machine learning-based techniques for future CDSS development.

Findings

Comprehensive categorization of synchronization techniques.

Identification of open challenges and research gaps.

Discussion on data-driven synchronization approaches.

Abstract

Cohesive Distributed Satellite Systems (CDSS) is a key enabling technology for the future of remote sensing and communication missions. However, they have to meet strict synchronization requirements before their use is generalized. When clock or local oscillator signals are generated locally at each of the distributed nodes, achieving exact synchronization in absolute phase, frequency, and time is a complex problem. In addition, satellite systems have significant resource constraints, especially for small satellites, which are envisioned to be part of the future CDSS. Thus, the development of precise, robust, and resource-efficient synchronization techniques is essential for the advancement of future CDSS. In this context, this survey aims to summarize and categorize the most relevant results on synchronization techniques for DSS. First, some important architecture and system concepts are defined. Then, the synchronization methods reported in the literature are reviewed and categorized. This article also provides an extensive list of applications and examples of synchronization techniques for DSS in addition to the most significant advances in other operations closely related to synchronization, such as inter-satellite ranging and relative position. The survey also provides a discussion on emerging data-driven synchronization techniques based on ML. Finally, a compilation of current research activities and potential research topics is proposed, identifying problems and open challenges that can be useful for researchers in the field.