A Study of Distributed Earth Observation Satellites Mission Scheduling Method Based on Game-Negotiation Mechanism
While monolithic giant earth observation satellites still have obvious advantages in regularity and accuracy, distributed satellite systems are providing increased flexibility, enhanced robustness, and improved responsiveness to structural and environmental changes. Due to increased system size and...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/19/6660 |
| _version_ | 1797515714006876160 |
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| author | Lihao Liu Zhenghong Dong Haoxiang Su Dingzhan Yu |
| author_facet | Lihao Liu Zhenghong Dong Haoxiang Su Dingzhan Yu |
| author_sort | Lihao Liu |
| collection | DOAJ |
| description | While monolithic giant earth observation satellites still have obvious advantages in regularity and accuracy, distributed satellite systems are providing increased flexibility, enhanced robustness, and improved responsiveness to structural and environmental changes. Due to increased system size and more complex applications, traditional centralized methods have difficulty in integrated management and rapid response needs of distributed systems. Aiming to efficient missions scheduling in distributed earth observation satellite systems, this paper addresses the problem through a networked game model based on a game-negotiation mechanism. In this model, each satellite is viewed as a “rational” player who continuously updates its own “action” through cooperation with neighbors until a Nash Equilibria is reached. To handle static and dynamic scheduling problems while cooperating with a distributed mission scheduling algorithm, we present an adaptive particle swarm optimization algorithm and adaptive tabu-search algorithm, respectively. Experimental results show that the proposed method can flexibly handle situations of different scales in static scheduling, and the performance of the algorithm will not decrease significantly as the problem scale increases; dynamic scheduling can be well accomplished with high observation payoff while maintaining the stability of the initial plan, which demonstrates the advantages of the proposed methods. |
| first_indexed | 2024-03-10T06:51:12Z |
| format | Article |
| id | doaj.art-704920eb38d847da84442293c09fe935 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T06:51:12Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-704920eb38d847da84442293c09fe9352023-11-22T16:49:28ZengMDPI AGSensors1424-82202021-10-012119666010.3390/s21196660A Study of Distributed Earth Observation Satellites Mission Scheduling Method Based on Game-Negotiation MechanismLihao Liu0Zhenghong Dong1Haoxiang Su2Dingzhan Yu3Graduate School, Space Engineering University, Beijing 101416, ChinaSchool of Space Information, Space Engineering University, Beijing 101416, ChinaGraduate School, Space Engineering University, Beijing 101416, ChinaGraduate School, Space Engineering University, Beijing 101416, ChinaWhile monolithic giant earth observation satellites still have obvious advantages in regularity and accuracy, distributed satellite systems are providing increased flexibility, enhanced robustness, and improved responsiveness to structural and environmental changes. Due to increased system size and more complex applications, traditional centralized methods have difficulty in integrated management and rapid response needs of distributed systems. Aiming to efficient missions scheduling in distributed earth observation satellite systems, this paper addresses the problem through a networked game model based on a game-negotiation mechanism. In this model, each satellite is viewed as a “rational” player who continuously updates its own “action” through cooperation with neighbors until a Nash Equilibria is reached. To handle static and dynamic scheduling problems while cooperating with a distributed mission scheduling algorithm, we present an adaptive particle swarm optimization algorithm and adaptive tabu-search algorithm, respectively. Experimental results show that the proposed method can flexibly handle situations of different scales in static scheduling, and the performance of the algorithm will not decrease significantly as the problem scale increases; dynamic scheduling can be well accomplished with high observation payoff while maintaining the stability of the initial plan, which demonstrates the advantages of the proposed methods.https://www.mdpi.com/1424-8220/21/19/6660EO satellitedistributed satellite systemdistributed mission scheduling |
| spellingShingle | Lihao Liu Zhenghong Dong Haoxiang Su Dingzhan Yu A Study of Distributed Earth Observation Satellites Mission Scheduling Method Based on Game-Negotiation Mechanism Sensors EO satellite distributed satellite system distributed mission scheduling |
| title | A Study of Distributed Earth Observation Satellites Mission Scheduling Method Based on Game-Negotiation Mechanism |
| title_full | A Study of Distributed Earth Observation Satellites Mission Scheduling Method Based on Game-Negotiation Mechanism |
| title_fullStr | A Study of Distributed Earth Observation Satellites Mission Scheduling Method Based on Game-Negotiation Mechanism |
| title_full_unstemmed | A Study of Distributed Earth Observation Satellites Mission Scheduling Method Based on Game-Negotiation Mechanism |
| title_short | A Study of Distributed Earth Observation Satellites Mission Scheduling Method Based on Game-Negotiation Mechanism |
| title_sort | study of distributed earth observation satellites mission scheduling method based on game negotiation mechanism |
| topic | EO satellite distributed satellite system distributed mission scheduling |
| url | https://www.mdpi.com/1424-8220/21/19/6660 |
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