Bayesian Adaptive Extended Kalman-Based Orbit Determination for Optical Observation Satellites
As the number of satellites and amount of space debris in Low-Earth orbit (LEO) increase, high-precision orbit determination is crucial for ensuring the safe operation of spacecraft and maintaining space situational awareness. However, ground-based optical observations are constrained by limited arc...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/8/2527 |
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| author | Yang Guo Qinghao Pang Xianlong Yin Xueshu Shi Zhengxu Zhao Jian Sun Jinsheng Wang |
| author_facet | Yang Guo Qinghao Pang Xianlong Yin Xueshu Shi Zhengxu Zhao Jian Sun Jinsheng Wang |
| author_sort | Yang Guo |
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| description | As the number of satellites and amount of space debris in Low-Earth orbit (LEO) increase, high-precision orbit determination is crucial for ensuring the safe operation of spacecraft and maintaining space situational awareness. However, ground-based optical observations are constrained by limited arc-segment angular data and dynamic noise interference, and the traditional Extended Kalman Filter (EKF) struggles to meet the accuracy and robustness requirements in complex orbital environments. To address these challenges, this paper proposes a Bayesian Adaptive Extended Kalman Filter (BAEKF), which synergistically optimizes track determination through dynamic noise covariance adjustment and Bayesian a posteriori probability correction. Experiments demonstrate that the average root mean square error (RMSE) of BAEKF is reduced by 34.7% compared to the traditional EKF, effectively addressing EKF’s accuracy and stability issues in nonlinear systems. The RMSE values of UKF, RBFNN, and GPR also show improvement, providing a reliable solution for high-precision orbital determination using optical observation. |
| format | Article |
| id | doaj-art-255ba4cd5baf4e538997bc1a73358b1a |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-255ba4cd5baf4e538997bc1a73358b1a2025-08-20T02:18:10ZengMDPI AGSensors1424-82202025-04-01258252710.3390/s25082527Bayesian Adaptive Extended Kalman-Based Orbit Determination for Optical Observation SatellitesYang Guo0Qinghao Pang1Xianlong Yin2Xueshu Shi3Zhengxu Zhao4Jian Sun5Jinsheng Wang6Shandong Key Laboratory of Space Debris Monitoring and Low-Orbit Satellite Networking, Qingdao University of Technology, Qingdao 266520, ChinaShandong Key Laboratory of Space Debris Monitoring and Low-Orbit Satellite Networking, Qingdao University of Technology, Qingdao 266520, ChinaShandong Key Laboratory of Space Debris Monitoring and Low-Orbit Satellite Networking, Qingdao University of Technology, Qingdao 266520, ChinaKey Laboratory of Intelligent Space TT&O (Space Engineering University), Ministry of Education, Beijing 101416, ChinaShandong Key Laboratory of Space Debris Monitoring and Low-Orbit Satellite Networking, Qingdao University of Technology, Qingdao 266520, ChinaNational Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, ChinaUniverse Kingdom Beijing Technology Co., Ltd., Beijing 100094, ChinaAs the number of satellites and amount of space debris in Low-Earth orbit (LEO) increase, high-precision orbit determination is crucial for ensuring the safe operation of spacecraft and maintaining space situational awareness. However, ground-based optical observations are constrained by limited arc-segment angular data and dynamic noise interference, and the traditional Extended Kalman Filter (EKF) struggles to meet the accuracy and robustness requirements in complex orbital environments. To address these challenges, this paper proposes a Bayesian Adaptive Extended Kalman Filter (BAEKF), which synergistically optimizes track determination through dynamic noise covariance adjustment and Bayesian a posteriori probability correction. Experiments demonstrate that the average root mean square error (RMSE) of BAEKF is reduced by 34.7% compared to the traditional EKF, effectively addressing EKF’s accuracy and stability issues in nonlinear systems. The RMSE values of UKF, RBFNN, and GPR also show improvement, providing a reliable solution for high-precision orbital determination using optical observation.https://www.mdpi.com/1424-8220/25/8/2527orbit determinationoptical observationsextended Kalman filterBayesian |
| spellingShingle | Yang Guo Qinghao Pang Xianlong Yin Xueshu Shi Zhengxu Zhao Jian Sun Jinsheng Wang Bayesian Adaptive Extended Kalman-Based Orbit Determination for Optical Observation Satellites Sensors orbit determination optical observations extended Kalman filter Bayesian |
| title | Bayesian Adaptive Extended Kalman-Based Orbit Determination for Optical Observation Satellites |
| title_full | Bayesian Adaptive Extended Kalman-Based Orbit Determination for Optical Observation Satellites |
| title_fullStr | Bayesian Adaptive Extended Kalman-Based Orbit Determination for Optical Observation Satellites |
| title_full_unstemmed | Bayesian Adaptive Extended Kalman-Based Orbit Determination for Optical Observation Satellites |
| title_short | Bayesian Adaptive Extended Kalman-Based Orbit Determination for Optical Observation Satellites |
| title_sort | bayesian adaptive extended kalman based orbit determination for optical observation satellites |
| topic | orbit determination optical observations extended Kalman filter Bayesian |
| url | https://www.mdpi.com/1424-8220/25/8/2527 |
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