It is increasingly common for satellites to lose connection with the ground stations on Earth with which they communicate, due to signal interruptions from the Earth's ionosphere and magnetosphere. Given the important roles that satellites play in national defense, public safety, and worldwide communications, finding ways to determine satellite trajectories in such situations is a crucially important task. In this paper, we demonstrate the efficacy of a novel computer vision based approach, which relies on earth imagery taken by the satellite itself, to determine the orbit of a satellite that has lost contact with its ground stations. We empirically observe significant improvements by more than an order of magnitude, over the present state of the art approach, namely, the Gibbs method for an initial orbit estimate with the Kalman filter for differential error correction. We further investigate the performance of the approach by comparing various neural networks, namely, ResNet50, ResNet101, VGG19, VGG16, AlexNet, and CoAtNet4.

本文报道了一种基于卫星本身所拍摄的地球图像的计算机视觉方法，用以确定与地面站失去联系的卫星的轨道，相较现有的Gibbs方法，该方法使初始轨道估计与Kalman滤波器进行差分误差校正的性能得到充分提升，并通过比较ResNet50、ResNet101、VGG19、VGG16、AlexNet和CoAtNet4等各种神经网络的性能进行了进一步的研究。

利用卫星图像的深度卷积网络进行低地球卫星轨道确定