CSIR-Net: a continuous spatio-temporal information redundancy network based on structural priors for satellite telemetry data reliability enhancement

Enhancing satellite telemetry reliability is essential for status assessment and operational management. However, telemetry data have complex spatio-temporal dependencies and diverse missing patterns, making it challenging for traditional methods with discrete modeling, fixed parameters, and static prediction steps to recover. To address these, this paper proposes a continuous spatio-temporal information redundancy network that transforms degraded discrete data recovery into timestamp-aware continuous-trajectory inference with uncertainty-calibrated selection. Specifically, a long short-term memory network is reconstructed using neural stochastic differential equations to model hidden-state evolution in continuous time, and Ito integrals are incorporated to characterize uneven observation gaps, thereby enabling recovery at arbitrary timestamps. Additionally, time-varying signals and Brownian perturbations are integrated into the model to allow dynamic adjustment of parameters with the input, thereby improving the ability to recover multiple evolving features. Finally, a structure-prior-guided hierarchical graph is constructed to fuse multi-scale dependencies while suppressing redundant error propagation, and multi-head graph attention is used to derive adaptive confidence intervals for selective recovery, thereby improving telemetry reliability without obscuring novel patterns. Experiments on five datasets show the proposed method improves recovery accuracy by 27.7% over the best baseline, achieves 15.2–78.5 ms batch latency, and has strong potential for real-time large-scale telemetry reliability.