空间碎片
人工智能
计算机科学
卷积神经网络
卫星
深度学习
美国宇航局深空网络
对象(语法)
基础(拓扑)
学习迁移
算法
计算机视觉
空格(标点符号)
模式识别(心理学)
碎片
数学
物理
天文
航天器
气象学
操作系统
数学分析
作者
T. Liu,K. U. Schreiber
标识
DOI:10.1016/j.actaastro.2021.05.008
摘要
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.
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