Low-Light Micro-Vibration Sensing on Satellite Platforms via Physical Encoding Self-Supervised Learning

Micro-vibrations of satellite platforms can reduce the pointing accuracy of space optical communication links, leading to a reduction in link margin and even link interruption. Traditional methods rely on accelerometers to obtain vibration labels, leading to hardware deployment difficulties and additional energy overhead in space or power constrained scenarios. Here, we propose a physics-encoded self-supervised vibration sensing model that directly recovers vibration signals from time-series images of the lunar surface without external sensors. The model consists of an optical flow module, a convolutional network, and a memory network, formulating vibration sensing as a physical inversion problem constrained by an image reconstruction process. This approach extracts the textural features of the lunar surface and the temporal dynamics characteristics of platform vibrations, achieving end-to-end high-precision vibration sensing. In simulation experiments, it attains excellent performance, with a coefficient of determination (R2) of 0.9975, a mean absolute error (MAE) of 0.0727, and a root mean square error (RMSE) of 0.0821. Furthermore, experiments on a real vibration platform validate the engineering applicability of the model, achieving an R2 of 0.9910, an MAE of 0.1180, and an RMSE of 0.1463, with predicted values closely matching the ground truth. The proposed model exhibits sub-pixel-level accuracy and excellent generalization capability in both simulated and real-world experimental scenarios, providing an effective solution for visual sensing of micro-vibrations on satellite platforms in space optical communication.