A New Method Based on a Multilayer Perceptron Network to Determine In-Orbit Satellite Attitude for Spacecrafts without Active ADCS Like UVSQ-SAT
Climate change is largely determined by the radiation budget imbalance at the Top Of the Atmosphere (TOA), which is generated by the increasing concentrations of greenhouse gases (GHGs). As a result, the Earth Energy Imbalance (EEI) is considered as an Essential Climate Variable (ECV) that has to be...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/6/1185 |
| _version_ | 1827697136661168128 |
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| author | Adrien Finance Mustapha Meftah Christophe Dufour Thomas Boutéraon Slimane Bekki Alain Hauchecorne Philippe Keckhut Alain Sarkissian Luc Damé Antoine Mangin |
| author_facet | Adrien Finance Mustapha Meftah Christophe Dufour Thomas Boutéraon Slimane Bekki Alain Hauchecorne Philippe Keckhut Alain Sarkissian Luc Damé Antoine Mangin |
| author_sort | Adrien Finance |
| collection | DOAJ |
| description | Climate change is largely determined by the radiation budget imbalance at the Top Of the Atmosphere (TOA), which is generated by the increasing concentrations of greenhouse gases (GHGs). As a result, the Earth Energy Imbalance (EEI) is considered as an Essential Climate Variable (ECV) that has to be monitored continuously from space. However, accurate TOA radiation measurements remain very challenging. Ideally, EEI monitoring should be performed with a constellation of satellites in order to resolve as much as possible spatio-temporal fluctuations in EEI which contain important information on the underlying mechanisms driving climate change. The monitoring of EEI and its components (incoming solar, reflected solar, and terrestrial infrared fluxes) is the main objective of the UVSQ-SAT pathfinder nanosatellite, the first of its kind in the construction of a future constellation. UVSQ-SAT does not have an active determination system of its orientation with respect to the Sun and the Earth (i.e., the so-called attitude), a prerequisite in the calculation of EEI from the satellite radiation measurements. We present a new effective method to determine the UVSQ-SAT’s in-orbit attitude using its housekeeping and scientific sensors measurements and a well-established deep learning algorithm. One of the goals is to estimate the satellite attitude with a sufficient accuracy for retrieving the radiative fluxes (incoming solar, reflected solar, terrestrial infrared) on each face of the satellite with an uncertainty of less than ±5 Wm<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup></semantics></math></inline-formula> (1<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>σ</mi></semantics></math></inline-formula>). This new method can be extended to any other satellites with no active attitude determination or control system. To test the accuracy of the method, a ground-based calibration experiment with different attitudes is performed using the Sun as the radiative flux reference. Based on the deep learning estimation of the satellite ground-based attitude, the uncertainty on the solar flux retrieval is about ±16 Wm<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup></semantics></math></inline-formula> (1<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>σ</mi></semantics></math></inline-formula>). The quality of the retrieval is mainly limited by test conditions and the number of data samples used in training the deep learning system during the ground-based calibration. The expected increase in the number of training data samples will drastically decrease the uncertainty in the retrieved radiative fluxes. A very similar algorithm will be implemented and used in-orbit for UVSQ-SAT. |
| first_indexed | 2024-03-10T13:02:59Z |
| format | Article |
| id | doaj.art-7e53f5284e27434f9f09b994bc7f67a6 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T13:02:59Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7e53f5284e27434f9f09b994bc7f67a62023-11-21T11:22:25ZengMDPI AGRemote Sensing2072-42922021-03-01136118510.3390/rs13061185A New Method Based on a Multilayer Perceptron Network to Determine In-Orbit Satellite Attitude for Spacecrafts without Active ADCS Like UVSQ-SATAdrien Finance0Mustapha Meftah1Christophe Dufour2Thomas Boutéraon3Slimane Bekki4Alain Hauchecorne5Philippe Keckhut6Alain Sarkissian7Luc Damé8Antoine Mangin9Université de Versailles Saint-Quentin-en-Yvelines, Université Paris-Saclay, Sorbonne Université (SU), CNRS, LATMOS, 11 Boulevard d’Alembert, 78280 Guyancourt, FranceUniversité de Versailles Saint-Quentin-en-Yvelines, Université Paris-Saclay, Sorbonne Université (SU), CNRS, LATMOS, 11 Boulevard d’Alembert, 78280 Guyancourt, FranceUniversité de Versailles Saint-Quentin-en-Yvelines, Université Paris-Saclay, Sorbonne Université (SU), CNRS, LATMOS, 11 Boulevard d’Alembert, 78280 Guyancourt, FranceUniversité de Versailles Saint-Quentin-en-Yvelines, Université Paris-Saclay, Sorbonne Université (SU), CNRS, LATMOS, 11 Boulevard d’Alembert, 78280 Guyancourt, FranceUniversité de Versailles Saint-Quentin-en-Yvelines, Université Paris-Saclay, Sorbonne Université (SU), CNRS, LATMOS, 11 Boulevard d’Alembert, 78280 Guyancourt, FranceUniversité de Versailles Saint-Quentin-en-Yvelines, Université Paris-Saclay, Sorbonne Université (SU), CNRS, LATMOS, 11 Boulevard d’Alembert, 78280 Guyancourt, FranceUniversité de Versailles Saint-Quentin-en-Yvelines, Université Paris-Saclay, Sorbonne Université (SU), CNRS, LATMOS, 11 Boulevard d’Alembert, 78280 Guyancourt, FranceUniversité de Versailles Saint-Quentin-en-Yvelines, Université Paris-Saclay, Sorbonne Université (SU), CNRS, LATMOS, 11 Boulevard d’Alembert, 78280 Guyancourt, FranceUniversité de Versailles Saint-Quentin-en-Yvelines, Université Paris-Saclay, Sorbonne Université (SU), CNRS, LATMOS, 11 Boulevard d’Alembert, 78280 Guyancourt, FranceACRI-ST—CERGA, 10 Avenue Nicolas Copernic, 06130 Grasse, FranceClimate change is largely determined by the radiation budget imbalance at the Top Of the Atmosphere (TOA), which is generated by the increasing concentrations of greenhouse gases (GHGs). As a result, the Earth Energy Imbalance (EEI) is considered as an Essential Climate Variable (ECV) that has to be monitored continuously from space. However, accurate TOA radiation measurements remain very challenging. Ideally, EEI monitoring should be performed with a constellation of satellites in order to resolve as much as possible spatio-temporal fluctuations in EEI which contain important information on the underlying mechanisms driving climate change. The monitoring of EEI and its components (incoming solar, reflected solar, and terrestrial infrared fluxes) is the main objective of the UVSQ-SAT pathfinder nanosatellite, the first of its kind in the construction of a future constellation. UVSQ-SAT does not have an active determination system of its orientation with respect to the Sun and the Earth (i.e., the so-called attitude), a prerequisite in the calculation of EEI from the satellite radiation measurements. We present a new effective method to determine the UVSQ-SAT’s in-orbit attitude using its housekeeping and scientific sensors measurements and a well-established deep learning algorithm. One of the goals is to estimate the satellite attitude with a sufficient accuracy for retrieving the radiative fluxes (incoming solar, reflected solar, terrestrial infrared) on each face of the satellite with an uncertainty of less than ±5 Wm<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup></semantics></math></inline-formula> (1<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>σ</mi></semantics></math></inline-formula>). This new method can be extended to any other satellites with no active attitude determination or control system. To test the accuracy of the method, a ground-based calibration experiment with different attitudes is performed using the Sun as the radiative flux reference. Based on the deep learning estimation of the satellite ground-based attitude, the uncertainty on the solar flux retrieval is about ±16 Wm<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup></semantics></math></inline-formula> (1<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>σ</mi></semantics></math></inline-formula>). The quality of the retrieval is mainly limited by test conditions and the number of data samples used in training the deep learning system during the ground-based calibration. The expected increase in the number of training data samples will drastically decrease the uncertainty in the retrieved radiative fluxes. A very similar algorithm will be implemented and used in-orbit for UVSQ-SAT.https://www.mdpi.com/2072-4292/13/6/1185climateearth energy imbalancesatelliteremote sensorsdeep learning method |
| spellingShingle | Adrien Finance Mustapha Meftah Christophe Dufour Thomas Boutéraon Slimane Bekki Alain Hauchecorne Philippe Keckhut Alain Sarkissian Luc Damé Antoine Mangin A New Method Based on a Multilayer Perceptron Network to Determine In-Orbit Satellite Attitude for Spacecrafts without Active ADCS Like UVSQ-SAT Remote Sensing climate earth energy imbalance satellite remote sensors deep learning method |
| title | A New Method Based on a Multilayer Perceptron Network to Determine In-Orbit Satellite Attitude for Spacecrafts without Active ADCS Like UVSQ-SAT |
| title_full | A New Method Based on a Multilayer Perceptron Network to Determine In-Orbit Satellite Attitude for Spacecrafts without Active ADCS Like UVSQ-SAT |
| title_fullStr | A New Method Based on a Multilayer Perceptron Network to Determine In-Orbit Satellite Attitude for Spacecrafts without Active ADCS Like UVSQ-SAT |
| title_full_unstemmed | A New Method Based on a Multilayer Perceptron Network to Determine In-Orbit Satellite Attitude for Spacecrafts without Active ADCS Like UVSQ-SAT |
| title_short | A New Method Based on a Multilayer Perceptron Network to Determine In-Orbit Satellite Attitude for Spacecrafts without Active ADCS Like UVSQ-SAT |
| title_sort | new method based on a multilayer perceptron network to determine in orbit satellite attitude for spacecrafts without active adcs like uvsq sat |
| topic | climate earth energy imbalance satellite remote sensors deep learning method |
| url | https://www.mdpi.com/2072-4292/13/6/1185 |
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