Accurate time transfer by time of flight measurements via diffuse reflections on passive orbiting space debris targets requires a selection of suitable objects out of a large catalogue of debris items. In this paper, we report on our development of an automatic classification system of space objects based on photometric observations of sun illuminated satellite and debris items from the Mini–Mega TORTORA (MMT) system observation data base by a deep learning algorithm. A deep neural network model based on a convolutional long short-term memory network has been designed to identify four different object categories with a test accuracy of over 85%. The method is also suitable for an automated analysis of the temporal evolution of the orbit motion of specific space objects.

通过在被动轨道空间碎片目标上进行漫反射来实现飞行时间测量的准确时间传递，需要从大量的碎片项目中选择合适的物体。在本文中，我们通过深度学习算法，基于Mini-Mega TORTORA（MMT）系统观测数据库中对太阳照亮的卫星和碎片的光度观测，报告了我们的空间物体自动分类系统的开发。设计了基于卷积长期短期记忆网络的深度神经网络模型，以识别四种不同的对象类别，其测试准确度超过85％。该方法还适合于对特定空间物体的轨道运动的时间演变进行自动分析。