A multi-satellite mission planning method based on k-means clustering
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A task planning, K-means technology, applied in instruments, complex mathematical operations, calculations, etc., can solve problems such as complex calculation process and many iterations
Active Publication Date: 2019-08-09
湖南国科轩宇信息科技有限公司
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[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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[0048] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways defined and covered by the claims.
[0049] The basic idea of the K-means clustering algorithm is to divide the N data in the data set into K classes, and the average vector from the data in each class to the center of the class is the shortest, which is also the distance from the point to the cluster point. Usually, the K-means clustering algorithm randomly selects K points as the cluster centers, calculates the distance from other points to each cluster center, and assigns each point to the class where the cluster center with the shortest distance is located. Find the center value of all data in a class to establish a new clustering point, and then continue to classify all points. After several iterations, until the clustering category of each point is stable, the K-means clustering...
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Abstract
The present invention provides a multi-satellite mission planning method based on K-means clustering, S1, collecting the user's mission requirements T={t 1 ,t 2 ,t 3 ...t n}, to obtain the set of orbital working hours of each sunlit area corresponding to all currently available satellites O={o 1 ,o 2 ,o 3 ,...o m}. S2, computing task t i to each element o in the set O j The distance Dis ij , forming a task t i The distance set D={d to the orbital set O i1 , d i2 , d i3 ... d in}, the task t i Clustering to the shortest orbital k,Dis ik =Min(D); S3, judging the current clustering scheme s k Whether it belongs to the set S={s 1 ,s 2 ,s 3 ,...s z}, if s k ∈S then output the clustering scheme s k , otherwise the scheme s k Add to the scheme set S, and return to step S2. The present invention quantifies these factors by analyzing the factors affecting multi-satellite task allocation, and combines the K-means clustering algorithm to plan a multi-satellite collaborative task allocation scheme, with fewer iterations and fast calculation speed, which can meet large-scale optimization problems Constrains the time complexity of the algorithm, and greatly improves the quality of imaging and the completion rate of tasks.
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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