Remote-Sensing Satellite Mission Scheduling Optimisation Method under Dynamic Mission Priorities

Mission scheduling is an essential function of the management control of remote-sensing satellite application systems. With the continuous development of remote-sensing satellite applications, mission scheduling faces significant challenges. Existing work has many inherent shortcomings in dealing wi...

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Bibliographic Details
Published in:Mathematics (Basel) 2024-06, Vol.12 (11), p.1704
Main Authors: Li, Xiuhong, Sun, Chongxiang, Fan, Huilong, Yang, Jiale
Format: Article
Language:English
Subjects:
Adaptability
Algorithms
Artificial satellites in remote sensing
Branch and bound methods
Deep learning
dynamic task priority
Efficiency
Genetic algorithms
Ground stations
Heuristic
Heuristic methods
Management
Mathematical optimization
Methods
Optimization techniques
Priority scheduling
real-time adaptability
Remote control
Remote sensing
remote-sensing satellite
Resource allocation
Rocket launches
Satellite communications
satellite scheduling
Scheduling
Scheduling (Management)
Task scheduling
Technology application
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Summary:Mission scheduling is an essential function of the management control of remote-sensing satellite application systems. With the continuous development of remote-sensing satellite applications, mission scheduling faces significant challenges. Existing work has many inherent shortcomings in dealing with dynamic task scheduling for remote-sensing satellites. In high-load and complex remote sensing task scenarios, there is low scheduling efficiency and a waste of resources. The paper proposes a scheduling method for remote-sensing satellite applications based on dynamic task prioritization. This paper combines the and Bound methodologies with an onboard task queue scheduling band in an active task prioritization context. A purpose-built emotional task priority-based scheduling blueprint is implemented to mitigate the flux and unpredictability characteristics inherent in the traditional satellite scheduling paradigm, improve scheduling efficiency, and fine-tune satellite resource allocation. Therefore, the Branch and Bound method in remote-sensing satellite task scheduling will significantly save space and improve efficiency. The experimental results show that comparing the technique to the three heuristic algorithms (GA, PSO, DE), the BnB method usually performs better in terms of the maximum value of the objective function, always finds a better solution, and reduces about 80% in terms of running time.
ISSN:2227-7390
2227-7390
DOI:10.3390/math12111704