The upcoming landscape of Earth Observation missions will defined by networked heterogeneous nanosatellite constellations required to meet strict mission requirements, such as revisit times and spatial resolution. However, scheduling satellite communications in these satellite networks through efficiently creating a global satellite Contact Plan (CP) is a complex task, with current solutions requiring ground-based coordination or being limited by onboard computational resources. The paper proposes a novel approach to overcome these challenges by modeling the constellations and CP as dynamic networks and employing graph-based techniques. The proposed method utilizes a state-of-the-art dynamic graph neural network to evaluate the performance of a given CP and update it using a heuristic algorithm based on simulated annealing. The trained neural network can predict the network delay with a mean absolute error of 3.6 minutes. Simulation results show that the proposed method can successfully design a contact plan for large satellite networks, improving the delay by 29.1%, similar to a traditional approach, while performing the objective evaluations 20x faster.

通过建模卫星星座和卫星接触计划为动态网络并采用基于图论的技术，提出了一种新方法来解决卫星网络通信调度的复杂性问题，通过仿真退火的启发式算法更新，推测网络延迟误差平均为3.6分钟，在提高29.1%延迟的同时，实现20倍的目标评估速度。

利用动态图神经网络实现异构纳米卫星星座的自主协作