کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8131940 | 1523269 | 2018 | 32 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Improving orbit prediction accuracy through supervised machine learning
ترجمه فارسی عنوان
بهبود دقت پیش بینی مدار را از طریق یادگیری دستگاه نظارت
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
پیش بینی مدار، شیء فضایی ساکن، نظارت بر یادگیری ماشین، ماشین بردار پشتیبانی،
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
علوم فضا و نجوم
چکیده انگلیسی
Due to the lack of information such as the space environment condition and resident space objects' (RSOs') body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs' trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: (1) the ML model can be used to improve the same RSO's orbit information that is not available during the learning process but shares the same time interval as the training data; (2) the ML model can be used to improve predictions of the same RSO at future epochs; and (3) the ML model based on a RSO can be applied to other RSOs that share some common features.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Advances in Space Research - Volume 61, Issue 10, 15 May 2018, Pages 2628-2646
Journal: Advances in Space Research - Volume 61, Issue 10, 15 May 2018, Pages 2628-2646
نویسندگان
Hao Peng, Xiaoli Bai,