AI 中文总结
该研究提出无地图方法，结合GP与MLP估计器及EphemerisWindow，从低轨卫星观测学习视距天际线，实现无需额外传感的主动切换准备。
AI 中文摘要
在非地面网络部署中，本地障碍物可能在卫星到达几何仰角掩模前阻断视距卫星链路，引发突发且无计划的切换。为解决该问题，本文提出一种无地图方法，用于从终端被动卫星信号观测得到的二元可用性标签中学习局部视距天际线，该天际线定义为仰角随方位角变化的障碍物轮廓。该问题被建模为方位角-仰角空间中的二元分类任务，其中天际线作为学习到的遮挡概率曲面的决策边界被提取。研究了两种互补估计器，即高斯过程（GP）分类器和带有循环方位角编码及蒙特卡洛弃权（不执行）不确定性指标的神经网络多层感知器（MLP）。随后，将学习到的遮挡曲面与卫星星历信息通过星历窗口（EphemerisWindow）结合，这是一种轨迹级预测方法，可在服务链路丢失前估计未来视距终止事件。结果表明，两种学习到的估计器相比经验 bracketing 方法均提升了天际线重建效果，且无需3D建筑地图、天空相机或额外环境传感即可实现主动切换准备。
英文摘要
In non-terrestrial network deployments, local obstructions may block line-of-sight satellite links before the satellite reaches the geometric elevation mask, causing abrupt and unplanned handovers. To address this limitation, this paper proposes a map-free method for learning the local LoS skyline, defined as the obstruction elevation over azimuth, from binary availability labels derived from passive satellite signal observations at the terminal. The problem is formulated as a binary classification task in the azimuth-elevation space, where the skyline is extracted as the decision boundary of the learned blockage probability surface. Two complementary estimators are investigated, namely a Gaussian Process (GP) classifier and a neural multilayer perceptron (MLP) with circular azimuth encoding and Monte Carlo Dropout uncertainty indicators. The learned obstruction surface is then combined with satellite ephemeris information through EphemerisWindow, a trajectory-level prediction method that estimates future LoS termination events before the serving link is lost. The results show that both learned estimators improve the skyline reconstruction compared with empirical bracketing and enable proactive handover preparation without requiring 3D building maps, sky cameras, or additional environmental sensing.
CommentsAccepted for presentation at the IEEE PIMRC 2026 Workshop. 6 pages
