Attitude Determination System for a Cubesat Experiencing Eclipse
In the context of Kalman filters, the predicted error covariance matrix <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi mathvariant="bold">P</mi><mrow><mi>k</mi>...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/20/8549 |
| _version_ | 1797572280586338304 |
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| author | Kesaobaka Mmopelwa Teddy Tumisang Ramodimo Oduetse Matsebe Bokamoso Basutli |
| author_facet | Kesaobaka Mmopelwa Teddy Tumisang Ramodimo Oduetse Matsebe Bokamoso Basutli |
| author_sort | Kesaobaka Mmopelwa |
| collection | DOAJ |
| description | In the context of Kalman filters, the predicted error covariance matrix <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi mathvariant="bold">P</mi><mrow><mi>k</mi><mo>+</mo><mn>1</mn></mrow></msub></semantics></math></inline-formula> and measurement noise covariance matrix <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="bold">R</mi></semantics></math></inline-formula> are used to represent the uncertainty of state variables and measurement noise, respectively. However, in real-world situations, these matrices may vary with time due to measurement faults. To address this issue in CubeSat attitude estimation, an adaptive extended Kalman filter has been proposed that can dynamically estimate the predicted error covariance matrix and measurement noise covariance matrix using an expectation-maximization approach. Simulation experiments have shown that this algorithm outperforms existing methods in terms of attitude estimation accuracy, particularly in sunlit and shadowed phases of the orbit, with the same filtering parameters and initial conditions. |
| first_indexed | 2024-03-10T20:54:59Z |
| format | Article |
| id | doaj.art-b3f50b1a7b454d51afb1b9f23efd2385 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:54:59Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b3f50b1a7b454d51afb1b9f23efd23852023-11-19T18:04:39ZengMDPI AGSensors1424-82202023-10-012320854910.3390/s23208549Attitude Determination System for a Cubesat Experiencing EclipseKesaobaka Mmopelwa0Teddy Tumisang Ramodimo1Oduetse Matsebe2Bokamoso Basutli3Department of Mechanical, Energy, and Industrial Engineering, Fauculty of Engineering, Botswana International University of Science and Technology, Private Bag 16, Palapye 10071, BotswanaDepartment of Mechanical, Energy, and Industrial Engineering, Fauculty of Engineering, Botswana International University of Science and Technology, Private Bag 16, Palapye 10071, BotswanaDepartment of Mechanical, Energy, and Industrial Engineering, Fauculty of Engineering, Botswana International University of Science and Technology, Private Bag 16, Palapye 10071, BotswanaDepartment of Electrical, Computer, and Telecommunications Engineering, Fauculty of Engineering, Botswana International University of Science and Technology, Private Bag 16, Palapye 10071, BotswanaIn the context of Kalman filters, the predicted error covariance matrix <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi mathvariant="bold">P</mi><mrow><mi>k</mi><mo>+</mo><mn>1</mn></mrow></msub></semantics></math></inline-formula> and measurement noise covariance matrix <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="bold">R</mi></semantics></math></inline-formula> are used to represent the uncertainty of state variables and measurement noise, respectively. However, in real-world situations, these matrices may vary with time due to measurement faults. To address this issue in CubeSat attitude estimation, an adaptive extended Kalman filter has been proposed that can dynamically estimate the predicted error covariance matrix and measurement noise covariance matrix using an expectation-maximization approach. Simulation experiments have shown that this algorithm outperforms existing methods in terms of attitude estimation accuracy, particularly in sunlit and shadowed phases of the orbit, with the same filtering parameters and initial conditions.https://www.mdpi.com/1424-8220/23/20/8549attitude estimationexpectation maximizationcubesatextended Kalman filteringattitude kinematics |
| spellingShingle | Kesaobaka Mmopelwa Teddy Tumisang Ramodimo Oduetse Matsebe Bokamoso Basutli Attitude Determination System for a Cubesat Experiencing Eclipse Sensors attitude estimation expectation maximization cubesat extended Kalman filtering attitude kinematics |
| title | Attitude Determination System for a Cubesat Experiencing Eclipse |
| title_full | Attitude Determination System for a Cubesat Experiencing Eclipse |
| title_fullStr | Attitude Determination System for a Cubesat Experiencing Eclipse |
| title_full_unstemmed | Attitude Determination System for a Cubesat Experiencing Eclipse |
| title_short | Attitude Determination System for a Cubesat Experiencing Eclipse |
| title_sort | attitude determination system for a cubesat experiencing eclipse |
| topic | attitude estimation expectation maximization cubesat extended Kalman filtering attitude kinematics |
| url | https://www.mdpi.com/1424-8220/23/20/8549 |
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