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

• Published in: Mathematics (Basel) Vol. 12; no. 11; p. 1704
• Main Authors: Li, Xiuhong, Sun, Chongxiang, Fan, Huilong, Yang, Jiale
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• 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; China
• DOI: 10.3390/math12111704
• ISSN: 2227-7390

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.