State estimation for low earth orbit satellite constellations using dynamic graph neural networks

With the continued deployment of large scale low earth orbit (LEO) satellite constellations, intelligent operation and maintenance (O&M) systems increasingly rely on telemetry centric time series data as their primary knowledge source. However, constrained jointly by ground station visibility and communication scheduling, telemetry exhibits pronounced asynchrony: high-frequency real-time telemetry (RTT) can only be acquired within short visibility windows, while low frequency delayed telemetry (DT) suffers from substantial latency, making it difficult to obtain a unified and accurate constellation level state estimate. To address this issue, this work proposes an intelligent state estimation method based on asynchronous telemetry fusion. The method first constructs a physics constrained dynamic graph using orbital dynamics and link reachability to characterize the time-varying constellation topology. On this basis, it designs a dual channel feature extraction architecture for RTT and DT and employs an attention mechanism for adaptive fusion, yielding a compact constellation level state vector. Under both real and simulated operating scenarios of a representative Walker constellation, this module is instantiated as a hybrid telemetry dynamic graph neural network (HT-DGNN) and compared against three representative baseline models. The results demonstrate that the proposed method achieves significant advantages in terms of estimation error, temporal consistency, and robustness to long-term evolution and observation gaps, providing smoother and more reliable state trajectories. This indicates good engineering adaptability and facilitates integration into monitoring, anomaly awareness, and mission scheduling workflows in constellation O&M.