A multi-satellite task planning method based on K-means clustering

Abstract

The invention provides a multi-satellite task planning method based on K-means clustering, the method comprises the steps of: S1, collecting task requirements of users T= {t1, t2, t3... Tn}, and obtaining an orbital working time set O= {o1, o2, o3,... Om} corresponding to each circle of sun illumination region of all currently available satellites;S 2, calculating the distance Disij between the task ti and each element oj in the set O, forming a distance set D= {di1, di2, di3... Din} between the task ti and the orbit set O, and clustering the task ti to the orbit k, Disik=Min (D) which is theshortest distance from the orbit set O; S3, judging whether the current clustering scheme sk belongs to the set S= {s1, s2, s3,... Sz}, outputting the clustering scheme sk if sk belongs to S, otherwise adding the scheme sk to the scheme set S, and returning to step S2. By analyzing the factors affecting the assignment of multi-satellite tasks and quantifying these factors and by using the K-meansclustering algorithm, a multi-satellite cooperative task assignment scheme is designed, which has fewer iterations and faster computational speed, and can meet the constraints of time complexity for large-scale optimization problems, and greatly improve the quality of imaging, and improve the completion rate of the task.

Description

technical field [0001] The invention relates to the technical field of satellite mission planning, in particular to a multi-satellite mission planning method based on K-means clustering. Background technique [0002] At the beginning of the development of imaging mission technology, due to the limited satellite load capacity and relatively few user missions, the imaging time and imaging angle of missions are relatively fixed, and mission planning problems are not prominent. With the development of imaging satellite technology and the increase in demand for ground image data, users' requirements for demand are also more complex. Satellites start to need to adjust the side view angle of remote sensing equipment for imaging. In the imaging process, various factors must be considered to meet user needs, and the earth observation satellites are scheduled and planned based on the overall optimization strategy. [0003] Simple reasoning and calculation in the existing technology c...

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Problems solved by technology

[0004] Aiming at the defects existing in the prior art, the present invention provides a multi-satellite mission planning method based on K-means clustering to solve the problem of multiple iterations, complex calculation process, multi-objective comprehensive optimization of imaging quality and mission completion rate

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