Research On Earth Observing Satellite Task Scheduling Method

With the enhancement of satellite maneuverability and high-speed development of imaging payloads,the imaging technology of Earth observation satellites has gradually matured,which has greatly improved the effectiveness of Earth observation.However,along with the increasing human demand for earth observation,the contradiction between the increasing observation demand and the limited data storage capacity of satellites has led to the planning and scheduling of earth observing satellites(EOS)gradually showing the trend of overload,which has increased the difficulty of problem solving.Therefore,at the satellite application level,there is an urgent need to analyze the scheduling characteristics of various EOS and study the corresponding scheduling methods to achieve the optimal scheduling of EOS.In this research,we analyze the scheduling characteristics of agile/non-agile EOS with optical/SAR payloads,and establish a scheduling model for EOS based on describing the problem as a constrained combinatorial optimization problem,taking the scheduling characteristics of various EOS under the integrated scheduling problem of imaging data transmission as the starting point.To meet the requirements of timeliness,stability and controllability of the EOS task planning and scheduling algorithm in engineering scenarios,this paper designs a deterministic EOS scheduling algorithm based on greedy strategy to realize the planning of in-orbit tasks such as satellite imaging,data transmission and storage release.Further,this paper considers the imaging characteristics of different EOS,gives the corresponding imaging window determination method,and designs a dichotomy-based imaging window adjustment method,which achieves the dissolution of task conflicts and increases the effective improvement of overall scheduling benefits.Meanwhile,to address the problem that deterministic scheduling methods can only obtain local optimal solutions and do not have the ability to find the best,this paper designs an improved binary particle swarm algorithm that introduces a hierarchical flow mechanism,and achieves an effective solution to the comprehensive scheduling problem of imaging data transmission by integrating and coding the imaging data transmission decisions and designing a neighborhood repair mechanism for handling scheduling constraints,and achieves significant algorithmic performance improvement.