A Machine Learning and Feature Engineering Approach for the Prediction of the Uncontrolled Re-Entry of Space Objects
The continuously growing number of objects orbiting around the Earth is expected to be accompanied by an increasing frequency of objects re-entering the Earth’s atmosphere. Many of these re-entries will be uncontrolled, making their prediction challenging and subject to several uncertainties. Tradit...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/10/3/297 |
| _version_ | 1797614125454458880 |
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| author | Francesco Salmaso Mirko Trisolini Camilla Colombo |
| author_facet | Francesco Salmaso Mirko Trisolini Camilla Colombo |
| author_sort | Francesco Salmaso |
| collection | DOAJ |
| description | The continuously growing number of objects orbiting around the Earth is expected to be accompanied by an increasing frequency of objects re-entering the Earth’s atmosphere. Many of these re-entries will be uncontrolled, making their prediction challenging and subject to several uncertainties. Traditionally, re-entry predictions are based on the propagation of the object’s dynamics using state-of-the-art modelling techniques for the forces acting on the object. However, modelling errors, particularly related to the prediction of atmospheric drag, may result in poor prediction accuracies. In this context, we explored the possibility of performing a paradigm shift, from a physics-based approach to a data-driven approach. To this aim, we present the development of a deep learning model for the re-entry prediction of uncontrolled objects in Low Earth Orbit (LEO). The model is based on a modified version of the Sequence-to-Sequence architecture and is trained on the average altitude profile as derived from a set of Two-Line Element (TLE) data of over 400 bodies. The novelty of the work consists in introducing in the deep learning model, alongside the average altitude, and three new input features: a drag-like coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>B</mi><mo>*</mo></msup></semantics></math></inline-formula>), the average solar index, and the area-to-mass ratio of the object. The developed model was tested on a set of objects studied in the Inter-Agency Space Debris Coordination Committee (IADC) campaigns. The results show that the best performances are obtained on bodies characterised by the same drag-like coefficient and eccentricity distribution as the training set. |
| first_indexed | 2024-03-11T07:04:40Z |
| format | Article |
| id | doaj.art-402d16e4cce64209890a5ac822663017 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-11T07:04:40Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-402d16e4cce64209890a5ac8226630172023-11-17T08:59:07ZengMDPI AGAerospace2226-43102023-03-0110329710.3390/aerospace10030297A Machine Learning and Feature Engineering Approach for the Prediction of the Uncontrolled Re-Entry of Space ObjectsFrancesco Salmaso0Mirko Trisolini1Camilla Colombo2Department of Space Science and Technology, Politecnico di Milano, Via La Masa 34, 10156 Milan, ItalyDepartment of Space Science and Technology, Politecnico di Milano, Via La Masa 34, 10156 Milan, ItalyDepartment of Space Science and Technology, Politecnico di Milano, Via La Masa 34, 10156 Milan, ItalyThe continuously growing number of objects orbiting around the Earth is expected to be accompanied by an increasing frequency of objects re-entering the Earth’s atmosphere. Many of these re-entries will be uncontrolled, making their prediction challenging and subject to several uncertainties. Traditionally, re-entry predictions are based on the propagation of the object’s dynamics using state-of-the-art modelling techniques for the forces acting on the object. However, modelling errors, particularly related to the prediction of atmospheric drag, may result in poor prediction accuracies. In this context, we explored the possibility of performing a paradigm shift, from a physics-based approach to a data-driven approach. To this aim, we present the development of a deep learning model for the re-entry prediction of uncontrolled objects in Low Earth Orbit (LEO). The model is based on a modified version of the Sequence-to-Sequence architecture and is trained on the average altitude profile as derived from a set of Two-Line Element (TLE) data of over 400 bodies. The novelty of the work consists in introducing in the deep learning model, alongside the average altitude, and three new input features: a drag-like coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>B</mi><mo>*</mo></msup></semantics></math></inline-formula>), the average solar index, and the area-to-mass ratio of the object. The developed model was tested on a set of objects studied in the Inter-Agency Space Debris Coordination Committee (IADC) campaigns. The results show that the best performances are obtained on bodies characterised by the same drag-like coefficient and eccentricity distribution as the training set.https://www.mdpi.com/2226-4310/10/3/297re-entry predictionsmachine learningdeep learningfeatures engineeringuncontrolled re-entry |
| spellingShingle | Francesco Salmaso Mirko Trisolini Camilla Colombo A Machine Learning and Feature Engineering Approach for the Prediction of the Uncontrolled Re-Entry of Space Objects Aerospace re-entry predictions machine learning deep learning features engineering uncontrolled re-entry |
| title | A Machine Learning and Feature Engineering Approach for the Prediction of the Uncontrolled Re-Entry of Space Objects |
| title_full | A Machine Learning and Feature Engineering Approach for the Prediction of the Uncontrolled Re-Entry of Space Objects |
| title_fullStr | A Machine Learning and Feature Engineering Approach for the Prediction of the Uncontrolled Re-Entry of Space Objects |
| title_full_unstemmed | A Machine Learning and Feature Engineering Approach for the Prediction of the Uncontrolled Re-Entry of Space Objects |
| title_short | A Machine Learning and Feature Engineering Approach for the Prediction of the Uncontrolled Re-Entry of Space Objects |
| title_sort | machine learning and feature engineering approach for the prediction of the uncontrolled re entry of space objects |
| topic | re-entry predictions machine learning deep learning features engineering uncontrolled re-entry |
| url | https://www.mdpi.com/2226-4310/10/3/297 |
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