利用机器学习开发用于基于广义光度的空间物体分类的高保真模拟器
Development of a High Fidelity Simulator for Generalised Photometric Based Space Object Classification using Machine Learning
摘要：
本文介绍了深度学习分类器开发的初始阶段，该分类器将高保真模拟光变曲线与迁移学习相结合，以提高在真实数据上训练的对象表征模型的性能。为了提高轨道预测的准确性，对RSO进行分类和定性是空间态势感知的一个重要目标。本文的具体重点是开发一个高保真仿真环境，用于生成逼真的光变曲线。该模拟器采用RSO的纹理几何模型以及对象星历表，并使用Blender生成RSO的照片级逼真图像，然后对其进行处理以提取光变曲线。模拟的光变曲线与从望远镜图像中提取的真实光变曲线进行了比较，为模拟环境提供了验证。未来的工作将涉及进一步的验证和使用模拟器来生成逼真的光变曲线数据集，以用于训练神经网络。
英文摘要：
This paper presents the initial stages in the development of a deep learning classifier for generalised Resident Space Object (RSO) characterisation that combines high-fidelity simulated light curves with transfer learning to improve the performance of object characterisation models that are trained on real data. The classification and characterisation of RSOs is a significant goal in Space Situational Awareness (SSA) in order to improve the accuracy of orbital predictions. The specific focus of this paper is the development of a high-fidelity simulation environment for generating realistic light curves. The simulator takes in a textured geometric model of an RSO as well as the objects ephemeris and uses Blender to generate photo-realistic images of the RSO that are then processed to extract the light curve. Simulated light curves have been compared with real light curves extracted from telescope imagery to provide validation for the simulation environment. Future work will involve further validation and the use of the simulator to generate a dataset of realistic light curves for the purpose of training neural networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
