Intelligent navigation for the cruise phase of solar system boundary exploration based on Q-learning EKF
Abstract With the continuous advancement of deep space exploration missions, the solar system boundary exploration mission is established as one of the China's most important deep space scientific exploration missions. The mission of the solar system boundary exploration has many challenges suc...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2023-12-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-023-01286-y |
| _version_ | 1797233198111916032 |
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| author | Wenjian Tao Jinxiu Zhang Hang Hu Juzheng Zhang Huijie Sun Zhankui Zeng Jianing Song Jihe Wang |
| author_facet | Wenjian Tao Jinxiu Zhang Hang Hu Juzheng Zhang Huijie Sun Zhankui Zeng Jianing Song Jihe Wang |
| author_sort | Wenjian Tao |
| collection | DOAJ |
| description | Abstract With the continuous advancement of deep space exploration missions, the solar system boundary exploration mission is established as one of the China's most important deep space scientific exploration missions. The mission of the solar system boundary exploration has many challenges such as ultra-remote detection distance, ultra-long operation time, and ultra-long communication delay. Therefore, the problem of high-precision autonomous navigation needs to be solved urgently. This paper designs an autonomous intelligent navigation method based on X-ray pulsars in the cruise phase, which estimate the motion state of the probe in real time. The proposed navigation method employs the Q-learning Extended Kalman filter (QLEKF) to improve navigation accuracy during long periods of self-determining running. The QLEKF selects automatically the error covariance matrix parameter of the process noise and the measurement noise by the reward mechanism of reinforcement learning. Compared to the traditional EKF and AEKF, the QLEKF improves the estimation accuracy of position and velocity. Finally, the simulation result demonstrates the effectiveness and the superiority of the intelligent navigation algorithm based on QLEKF, which can satisfy the high-precision navigation requirements in the cruise phase of the solar system boundary exploration. |
| first_indexed | 2024-04-24T16:12:21Z |
| format | Article |
| id | doaj.art-9b0a7f7045524ab3902ec0d66b1578eb |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-04-24T16:12:21Z |
| publishDate | 2023-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-9b0a7f7045524ab3902ec0d66b1578eb2024-03-31T11:39:35ZengSpringerComplex & Intelligent Systems2199-45362198-60532023-12-011022653267210.1007/s40747-023-01286-yIntelligent navigation for the cruise phase of solar system boundary exploration based on Q-learning EKFWenjian Tao0Jinxiu Zhang1Hang Hu2Juzheng Zhang3Huijie Sun4Zhankui Zeng5Jianing Song6Jihe Wang7School of Aeronautics and Astronautics, Sun Yat-Sen UniversitySchool of Aeronautics and Astronautics, Sun Yat-Sen UniversityMOE Key Laboratory of TianQin Mission, TianQin Research Center for Gravitational Physics and School of Physics and Astronomy, Frontiers Science Center for TianQin, Gravitational Wave Research Center of CNSA, Sun Yat-Sen University (Zhuhai Campus)MOE Key Laboratory of TianQin Mission, TianQin Research Center for Gravitational Physics and School of Physics and Astronomy, Frontiers Science Center for TianQin, Gravitational Wave Research Center of CNSA, Sun Yat-Sen University (Zhuhai Campus)School of Aeronautics and Astronautics, Sun Yat-Sen UniversityShanghai Academy of Aerospace TechnologyCity, University of LondonSchool of Aeronautics and Astronautics, Sun Yat-Sen UniversityAbstract With the continuous advancement of deep space exploration missions, the solar system boundary exploration mission is established as one of the China's most important deep space scientific exploration missions. The mission of the solar system boundary exploration has many challenges such as ultra-remote detection distance, ultra-long operation time, and ultra-long communication delay. Therefore, the problem of high-precision autonomous navigation needs to be solved urgently. This paper designs an autonomous intelligent navigation method based on X-ray pulsars in the cruise phase, which estimate the motion state of the probe in real time. The proposed navigation method employs the Q-learning Extended Kalman filter (QLEKF) to improve navigation accuracy during long periods of self-determining running. The QLEKF selects automatically the error covariance matrix parameter of the process noise and the measurement noise by the reward mechanism of reinforcement learning. Compared to the traditional EKF and AEKF, the QLEKF improves the estimation accuracy of position and velocity. Finally, the simulation result demonstrates the effectiveness and the superiority of the intelligent navigation algorithm based on QLEKF, which can satisfy the high-precision navigation requirements in the cruise phase of the solar system boundary exploration.https://doi.org/10.1007/s40747-023-01286-yQ-learningExtend Kalman filterX-ray pulsar intelligent navigationSolar system boundary explorationCruise phase |
| spellingShingle | Wenjian Tao Jinxiu Zhang Hang Hu Juzheng Zhang Huijie Sun Zhankui Zeng Jianing Song Jihe Wang Intelligent navigation for the cruise phase of solar system boundary exploration based on Q-learning EKF Complex & Intelligent Systems Q-learning Extend Kalman filter X-ray pulsar intelligent navigation Solar system boundary exploration Cruise phase |
| title | Intelligent navigation for the cruise phase of solar system boundary exploration based on Q-learning EKF |
| title_full | Intelligent navigation for the cruise phase of solar system boundary exploration based on Q-learning EKF |
| title_fullStr | Intelligent navigation for the cruise phase of solar system boundary exploration based on Q-learning EKF |
| title_full_unstemmed | Intelligent navigation for the cruise phase of solar system boundary exploration based on Q-learning EKF |
| title_short | Intelligent navigation for the cruise phase of solar system boundary exploration based on Q-learning EKF |
| title_sort | intelligent navigation for the cruise phase of solar system boundary exploration based on q learning ekf |
| topic | Q-learning Extend Kalman filter X-ray pulsar intelligent navigation Solar system boundary exploration Cruise phase |
| url | https://doi.org/10.1007/s40747-023-01286-y |
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