发表机构
Hamad Bin Khalifa University; Professionals for Smart Technology (PST); Qatar University（哈马德·本·哈利法大学; 智能技术专业人员（PST）; 卡塔尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对星间光链路精跟踪中的残余视轴运动问题，提出接收端物理信息残差数字孪生，结合物理模型与残差学习，在标称场景降低14.1%指向误差，并提升鲁棒性。
AI 中文摘要
星间激光链路的精跟踪必须补偿残余视轴（LOS）运动，尽管存在传感器噪声、振动、模型失配和执行器延迟。本文开发了一种接收端物理信息残差数字孪生（PIR-Twin），其结合了标称LOS转移、高斯光学、非线性四象限光电探测器观测以及扩展卡尔曼滤波同步。一个归一化自回归岭模型从独立的校准估计中学习转移失配，而已知的延迟精转向镜校正保持与物理LOS动力学分离。同步孪生在指令执行瞬间预测LOS，从而实现预测性精跟踪。一项受控评估比较了开环、反应式、标称预测式和PIR-Twin操作，使用不相交的调优、校准和测试实现。在标称场景下，PIR-Twin相对于反应式跟踪将RMS指向误差降低了14.1%，并提高了执行时间预测精度。扩展鲁棒性测试表明，所提方法在广泛的执行器延迟范围内以及在增加的时变LOS运动幅度下，无需残差模型重训练即可保持最低的平均指向误差。结果表明，校正系统性短时模型失配，而非仅依赖标称外推，是实现有效预测性精跟踪的关键机制。
英文摘要
Fine tracking in inter-satellite optical links must compensate residual line-of-sight (LOS) motion despite sensor noise, vibration, model mismatch, and actuator latency. This paper develops a receiver-side physics-informed residual digital twin (PIR-Twin) that combines a nominal LOS transition, Gaussian optics, a nonlinear quadrant-photodetector observation, and extended Kalman filter synchronization. A normalized autoregressive ridge model learns the transition mismatch from independent calibration estimates, while the known delayed fine-steering-mirror correction remains separate from the physical LOS dynamics. The synchronized twin predicts the LOS at the command-actuation instant and enables proactive fine tracking. A controlled evaluation compares open-loop, reactive, nominal-predictive, and PIR-Twin operation using disjoint tuning, calibration, and test realizations. Under the nominal scenario, PIR-Twin reduces RMS pointing error by 14.1% relative to reactive tracking and also improves actuation-time prediction accuracy. Extended robustness tests show that the proposed method retains the lowest mean pointing error over a broad actuator-delay range and under increased time-varying LOS-motion amplitudes without residual-model retraining. The results demonstrate that correcting systematic short-horizon model mismatch, rather than relying on nominal extrapolation alone, is the key mechanism enabling effective predictive fine tracking.
