A Unified Model for Multi-Satellite Imaging Mission Planning in Various Scenarios
Multi-satellite imaging mission planning (MSIMP) has been difficult in various scenarios due to the complex constraints of multi-satellite imaging, the wide area covered by target points, and the difficulty of achieving different mission requirements in a short period of time with limited satellite...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10122497/ |
| _version_ | 1797825661526605824 |
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| author | Xueying Yang Min Hu Rui Zhang Gang Huang |
| author_facet | Xueying Yang Min Hu Rui Zhang Gang Huang |
| author_sort | Xueying Yang |
| collection | DOAJ |
| description | Multi-satellite imaging mission planning (MSIMP) has been difficult in various scenarios due to the complex constraints of multi-satellite imaging, the wide area covered by target points, and the difficulty of achieving different mission requirements in a short period of time with limited satellite resources. In addressing this challenge, this work investigates multi-satellite imaging mission planning based on the Unified Plan Model and Improved Adaptive Differential Evolution algorithm (UPM-IADE). First, a unified model is built based on two scenarios: a large-scale imaging mission and an emergency support mission, and then a mission assignment framework is adaptively selected based on mission priority. Second, a monorail task synthesis method based on visible time windows is created to clarify the execution relationship between the satellite and the target point. Finally, an individual weight ranking rule is developed, and the weight is used to combine the fitness value ranking and diversity ranking into a final fitness value ranking, which is used to select individuals that satisfy the mutation requirements into the mutation strategy pool for adaptive mutation strategy selection. Experiments 1, 2, 3, and 4 have demonstrated that UPM-IADE can successfully resolve the imaging satellite mission planning for both scenarios while providing remarkable performance in terms of high mission benefit and rapid response. |
| first_indexed | 2024-03-13T10:57:06Z |
| format | Article |
| id | doaj.art-29bd0756795046699d82a34f7936359a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T10:57:06Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-29bd0756795046699d82a34f7936359a2023-05-16T23:00:18ZengIEEEIEEE Access2169-35362023-01-0111461444616310.1109/ACCESS.2023.327501610122497A Unified Model for Multi-Satellite Imaging Mission Planning in Various ScenariosXueying Yang0https://orcid.org/0000-0001-7443-9110Min Hu1https://orcid.org/0000-0002-2974-1107Rui Zhang2https://orcid.org/0000-0001-8681-0693Gang Huang3https://orcid.org/0000-0001-5909-7714Department of Aerospace Science and Technology, Space Engineering University, Beijing, ChinaDepartment of Aerospace Science and Technology, Space Engineering University, Beijing, ChinaDepartment of Aerospace Science and Technology, Space Engineering University, Beijing, ChinaDepartment of Aerospace Science and Technology, Space Engineering University, Beijing, ChinaMulti-satellite imaging mission planning (MSIMP) has been difficult in various scenarios due to the complex constraints of multi-satellite imaging, the wide area covered by target points, and the difficulty of achieving different mission requirements in a short period of time with limited satellite resources. In addressing this challenge, this work investigates multi-satellite imaging mission planning based on the Unified Plan Model and Improved Adaptive Differential Evolution algorithm (UPM-IADE). First, a unified model is built based on two scenarios: a large-scale imaging mission and an emergency support mission, and then a mission assignment framework is adaptively selected based on mission priority. Second, a monorail task synthesis method based on visible time windows is created to clarify the execution relationship between the satellite and the target point. Finally, an individual weight ranking rule is developed, and the weight is used to combine the fitness value ranking and diversity ranking into a final fitness value ranking, which is used to select individuals that satisfy the mutation requirements into the mutation strategy pool for adaptive mutation strategy selection. Experiments 1, 2, 3, and 4 have demonstrated that UPM-IADE can successfully resolve the imaging satellite mission planning for both scenarios while providing remarkable performance in terms of high mission benefit and rapid response.https://ieeexplore.ieee.org/document/10122497/Multi-satellite imaging mission planningglobal large-scale imaging missionemergency support missionadaptive differential evolution algorithmindividual weight ranking rule |
| spellingShingle | Xueying Yang Min Hu Rui Zhang Gang Huang A Unified Model for Multi-Satellite Imaging Mission Planning in Various Scenarios IEEE Access Multi-satellite imaging mission planning global large-scale imaging mission emergency support mission adaptive differential evolution algorithm individual weight ranking rule |
| title | A Unified Model for Multi-Satellite Imaging Mission Planning in Various Scenarios |
| title_full | A Unified Model for Multi-Satellite Imaging Mission Planning in Various Scenarios |
| title_fullStr | A Unified Model for Multi-Satellite Imaging Mission Planning in Various Scenarios |
| title_full_unstemmed | A Unified Model for Multi-Satellite Imaging Mission Planning in Various Scenarios |
| title_short | A Unified Model for Multi-Satellite Imaging Mission Planning in Various Scenarios |
| title_sort | unified model for multi satellite imaging mission planning in various scenarios |
| topic | Multi-satellite imaging mission planning global large-scale imaging mission emergency support mission adaptive differential evolution algorithm individual weight ranking rule |
| url | https://ieeexplore.ieee.org/document/10122497/ |
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