A Sliding Window Adaptive Filtering Algorithm for Autonomous Navigation of the Approach Phase of Deep Space Probe
When the deep space probe approaches the target planet, due to the rapid increase of the gravity of the target planet, the orbital dynamics model will have a rapid acceleration change. Because the noise covariance is not completely known, the traditional filtering algorithm cannot obtain the optimal...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Shanghai Jiao Tong University
2022-11-01
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| Series: | Shanghai Jiaotong Daxue xuebao |
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| Online Access: | http://xuebao.sjtu.edu.cn/article/2022/1006-2467/1006-2467-56-11-1461.shtml |
| _version_ | 1798014214906839040 |
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| author | ZHANG Wenjia, MA Xin |
| author_facet | ZHANG Wenjia, MA Xin |
| author_sort | ZHANG Wenjia, MA Xin |
| collection | DOAJ |
| description | When the deep space probe approaches the target planet, due to the rapid increase of the gravity of the target planet, the orbital dynamics model will have a rapid acceleration change. Because the noise covariance is not completely known, the traditional filtering algorithm cannot obtain the optimal estimation of navigation parameters, which is difficult to meet the performance requirements of the approach navigation system. In order to meet the requirements of high stability and accuracy of the system, a sliding window adaptive nonlinear filtering algorithm based on system noise covariance is proposed. By constructing the system noise covariance update function and using the sliding window to smooth the noise covariance, the errors caused by velocity noise and position noise are separated, the filter parameter information used is updated in real time, and the system noise covariance is adjusted adaptively. Taking the Mars probe as an example, the simulation results show that, compared with the traditional unscented Kalman filtering method, the position accuracy and velocity accuracy of the proposed method are improved by 90.97% and 66.17% respectively, which suppresses the rapidly changing integral error on the system model, and solves the divergence problem of the traditional filtering method. In addition, the influence of filtering period and window size on navigation performance is analyzed, which provides a feasible new adaptive filtering method for autonomous navigation of deep space exploration. |
| first_indexed | 2024-04-11T15:13:57Z |
| format | Article |
| id | doaj.art-80a33afcfccd45b8a1781f7ad8b90d39 |
| institution | Directory Open Access Journal |
| issn | 1006-2467 |
| language | zho |
| last_indexed | 2024-04-11T15:13:57Z |
| publishDate | 2022-11-01 |
| publisher | Editorial Office of Journal of Shanghai Jiao Tong University |
| record_format | Article |
| series | Shanghai Jiaotong Daxue xuebao |
| spelling | doaj.art-80a33afcfccd45b8a1781f7ad8b90d392022-12-22T04:16:32ZzhoEditorial Office of Journal of Shanghai Jiao Tong UniversityShanghai Jiaotong Daxue xuebao1006-24672022-11-0156111461146910.16183/j.cnki.jsjtu.2022.233A Sliding Window Adaptive Filtering Algorithm for Autonomous Navigation of the Approach Phase of Deep Space ProbeZHANG Wenjia, MA Xin0School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100091, ChinaWhen the deep space probe approaches the target planet, due to the rapid increase of the gravity of the target planet, the orbital dynamics model will have a rapid acceleration change. Because the noise covariance is not completely known, the traditional filtering algorithm cannot obtain the optimal estimation of navigation parameters, which is difficult to meet the performance requirements of the approach navigation system. In order to meet the requirements of high stability and accuracy of the system, a sliding window adaptive nonlinear filtering algorithm based on system noise covariance is proposed. By constructing the system noise covariance update function and using the sliding window to smooth the noise covariance, the errors caused by velocity noise and position noise are separated, the filter parameter information used is updated in real time, and the system noise covariance is adjusted adaptively. Taking the Mars probe as an example, the simulation results show that, compared with the traditional unscented Kalman filtering method, the position accuracy and velocity accuracy of the proposed method are improved by 90.97% and 66.17% respectively, which suppresses the rapidly changing integral error on the system model, and solves the divergence problem of the traditional filtering method. In addition, the influence of filtering period and window size on navigation performance is analyzed, which provides a feasible new adaptive filtering method for autonomous navigation of deep space exploration.http://xuebao.sjtu.edu.cn/article/2022/1006-2467/1006-2467-56-11-1461.shtmladaptive filteringkalman filterautonomous navigationdeep space exploration |
| spellingShingle | ZHANG Wenjia, MA Xin A Sliding Window Adaptive Filtering Algorithm for Autonomous Navigation of the Approach Phase of Deep Space Probe Shanghai Jiaotong Daxue xuebao adaptive filtering kalman filter autonomous navigation deep space exploration |
| title | A Sliding Window Adaptive Filtering Algorithm for Autonomous Navigation of the Approach Phase of Deep Space Probe |
| title_full | A Sliding Window Adaptive Filtering Algorithm for Autonomous Navigation of the Approach Phase of Deep Space Probe |
| title_fullStr | A Sliding Window Adaptive Filtering Algorithm for Autonomous Navigation of the Approach Phase of Deep Space Probe |
| title_full_unstemmed | A Sliding Window Adaptive Filtering Algorithm for Autonomous Navigation of the Approach Phase of Deep Space Probe |
| title_short | A Sliding Window Adaptive Filtering Algorithm for Autonomous Navigation of the Approach Phase of Deep Space Probe |
| title_sort | sliding window adaptive filtering algorithm for autonomous navigation of the approach phase of deep space probe |
| topic | adaptive filtering kalman filter autonomous navigation deep space exploration |
| url | http://xuebao.sjtu.edu.cn/article/2022/1006-2467/1006-2467-56-11-1461.shtml |
| work_keys_str_mv | AT zhangwenjiamaxin aslidingwindowadaptivefilteringalgorithmforautonomousnavigationoftheapproachphaseofdeepspaceprobe AT zhangwenjiamaxin slidingwindowadaptivefilteringalgorithmforautonomousnavigationoftheapproachphaseofdeepspaceprobe |