融合星间数据卸载的敏捷卫星任务规划  

Agile Satellite Task Planning with Intersatellite Data Offloading

作　　者：尉欢 刘志慧 王俊义[1] 董涛[2] WEI Huan;LIU Zhihui;WANG Junyi;DONG Tao(School of Information and Communication,Guilin University of Electronic Technology,Guilin 541004,China;State Key Laboratory of Space-Ground Integrated Information Technology,Beijing Institute of Satellite Information Engineering,Beijing 100095,China)

机构地区：[1]桂林电子科技大学信息与通信学院,广西桂林541004 [2]北京卫星信息工程研究所天地一体化信息技术国家重点实验室,北京100095

出　　处：《无线电工程》2022年第8期1473-1481,共9页Radio Engineering

基　　金：广西自然科学基金面上项目(2020GXNSFAA159105);认知无线电与信息处理教育部重点实验室开发基金资助项目(CRKL180201)。

摘　　要：现有仅考虑直接下传数据至地面站的任务规划策略已无法满足大幅增长的任务带来的内存压力,面对敏捷观测卫星星载内存受限的问题,提出了优先下传观测数据至地面站以及在星载内存资源紧张时将数据卸载至通信卫星星座的策略。为了解决任务规划的动态问题,建立了滚动双规划框架,提出了一种基于图适应度的改进遗传算法。仿真结果表明,该规划策略可在短时间内得到总观测收益最大的任务序列,有效提高卫星观测效率。The existing task planning strategy which only considers directly transferring data down to ground stations cannot meet the memory pressure caused by the greatly increased quantity of tasks.To solve the problem of limited onboard memory of smart observation satellites,a strategy is proposed to transfer observation data to ground stations first and offload data to communication satellite constellations when onboard memory resources are limited.In order to solve the dynamic problem of task planning,a rolling double-planning framework is established and an improved genetic algorithm based on graph fitness is proposed.Simulation results show that the planning strategy can obtain the task sequence with the maximum total observation benefit in a short time,and improve the efficiency of satellite observation.

关 键 词：任务规划 高动态 数据下传 数据卸载 遗传算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]