2023
Stevenson, Emma; Rodríguez-Fernández, Víctor; Urrutxua, Hodei; Camacho, David
Benchmarking deep learning approaches for all-vs-all conjunction screening Journal Article
In: Advances in Space Research, 2023.
Abstract | Links | BibTeX | Tags: Conjunction Assessment, Deep Learning, Filtering, Machine Learning, Orbits, Space, Space Debris, Space Environment Management, Space Situational Awareness, Space Traffic Management, Time Series
@article{nokey,
title = {Benchmarking deep learning approaches for all-vs-all conjunction screening},
author = {Emma Stevenson and Víctor Rodríguez-Fernández and Hodei Urrutxua and David Camacho},
doi = {https://doi.org/10.1016/j.asr.2023.01.036},
year = {2023},
date = {2023-01-23},
urldate = {2023-01-23},
journal = {Advances in Space Research},
abstract = {The all-vs-all problem, for which conjunctions are screened for over all possible sets of catalogued objects, is crucial for space traffic management and space situational awareness, but is a computational challenge owing to the vast and growing number of possible conjunction pairs. In this work, we present the application of deep learning techniques to this problem, framing conjunction screening as a machine learning classification task. We investigate the performance of different input data representations and model architectures on a realistic all-vs-all dataset, generated using the CNES BAS3E space surveillance simulation framework, and consisting of 170 million object pairs over a 7-day screening period. These approaches are benchmarked against operationally used classical filters in both screening capability and computational efficiency, and the ability of deep learning algorithms to cope and aid with the scales required for current and future operational all-vs-all scenarios is demonstrated.},
keywords = {Conjunction Assessment, Deep Learning, Filtering, Machine Learning, Orbits, Space, Space Debris, Space Environment Management, Space Situational Awareness, Space Traffic Management, Time Series},
pubstate = {published},
tppubtype = {article}
}
2021
Stevenson, Emma; Rodriguez-Fernandez, Victor; Urrutxua, Hodei; Morand, Vincent; Camacho, David
Self-supervised machine learning based approach to orbit modelling applied to space traffic management Proceedings Article
In: 11th International Association for the Advancement of Space Safety Conference (IAASS), (Virtual), Osaka, Japan, 2021.
Abstract | BibTeX | Tags: Machine Learning, Orbits, Self-supervised Learning, Space Debris, Space Traffic Management
@inproceedings{stevenson2021_iaass,
title = {Self-supervised machine learning based approach to orbit modelling applied to space traffic management},
author = {Emma Stevenson and Victor Rodriguez-Fernandez and Hodei Urrutxua and Vincent Morand and David Camacho},
year = {2021},
date = {2021-10-01},
booktitle = {11th International Association for the Advancement of Space Safety Conference (IAASS)},
address = {(Virtual), Osaka, Japan},
abstract = {This paper presents a novel methodology for improving the performance of machine learning based space traffic management tasks through the use of a pre-trained orbit model. Taking inspiration from BERT-like self-supervised language models in the field of natural language processing, we introduce ORBERT, and demonstrate the ability of such a model to leverage large quantities of readily available orbit data to learn meaningful representations that can be used to aid in downstream tasks. As a proof of concept of this approach we consider the task of all vs. all conjunction screening, phrased here as a machine learning time series classification task. We show that leveraging unlabelled orbit data leads to improved performance, and that the proposed approach can be particularly beneficial for tasks where the availability of labelled data is limited.},
keywords = {Machine Learning, Orbits, Self-supervised Learning, Space Debris, Space Traffic Management},
pubstate = {published},
tppubtype = {inproceedings}
}