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基于统一资源编码的成像卫星联合任务规划算法框架
引用本文:张佳唯,邢立宁,张玮,钱凤臣. 基于统一资源编码的成像卫星联合任务规划算法框架[J]. 控制与决策, 2022, 37(6): 1497-1504
作者姓名:张佳唯  邢立宁  张玮  钱凤臣
作者单位:国防科技大学信息通信学院,西安710106;国防科技大学系统工程学院,长沙410073
基金项目:国家自然科学基金项目(61773120).
摘    要:对于大量的卫星和地面站资源,随着观测任务与日俱增,如何高效安排对应的一体化成像数传活动成为提升卫星管控效能的关键.在综合考虑实际约束的基础上,建立数学模型详细描述成像卫星联合任务规划问题,通过采用统一资源编码的思想设计一种简单且易于理解的个体表示方法,并利用任务有效执行期的潜在冲突关系提出相互冲突任务集的概念以降低问题...

关 键 词:一体化成像数传  卫星管控  联合任务规划  统一资源编码  相互冲突任务集  大规模

A united mission planning algorithm framework based on uniform resource encoding for imaging satellites
ZHANG Jia-wei,XING Li-ning,ZHANG Wei,QIAN Feng-chen. A united mission planning algorithm framework based on uniform resource encoding for imaging satellites[J]. Control and Decision, 2022, 37(6): 1497-1504
Authors:ZHANG Jia-wei  XING Li-ning  ZHANG Wei  QIAN Feng-chen
Affiliation:College of Information and Communication,National University of Defense Technology,Xián 710106,China;College of Systems Engineering,National University of Defense Technology,Changsha 410073,China
Abstract:For a large number of earth observation satellites as well as ground station resources, how to effectively arrange the integrated imaging and data transmission activities plays a crucial role in improving the satellite management efficiency with the increasing quantity of observation missions. Based on comprehensive consideration of practical constraints, a mathematical model is established to describe the imaging satellite united mission planning problem in detail. A simple as well as comprehensible individual representation method is designed by adopting the idea of uniform resource encoding, and the conception of conflicting mission sets is proposed by utilizing the potential conflict relationship of the effective execution period for each mission to reduce the time complexity of solving this problem, thereby generating the corresponding algorithm framework. Finally, test instances demonstrate the effectiveness of the algorithm framework, which also shows its ability to obtain high-quality solutions within a limited time period for the large scale optimization instances.
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