针对卫星数传、测控任务的地面站资源调度问题，提出一种结合启发式方法的粒子群改进算法，对卫星的数传、测控任务进行一体化调度。首先分析卫星任务及地面站资源的约束条件，建立基于启发式规则的约束满足模型，筛选出较优的初始种群，然后设计一种结合启发式规则的粒子群算法求解。仿真对比实验表明，相对于常规调度算法(如遗传算法)，粒子群算法具有较好的寻优能力和收敛速度；相对于传统粒子群算法，结合启发式方法的粒子群改进算法具有更好的寻优能力、收敛速度和稳定性。
樊慧晶
,
章文毅
,
田妙苗
,
马广彬
,
程博
. 基于粒子群算法的卫星任务地面站资源调度方法[J]. 中国科学院大学学报, 2022
, 39(6)
: 801
-808
.
DOI: 10.7523/j.ucas.2021.0012
In view of the problem of ground station resource scheduling for satellite data transmission and telemetry, tracing and control (TT&C) tasks, this paper proposes a modified particle swarm optimization (PSO) algorithm combined with heuristic methods to carry out integrated scheduling of satellite data transmission and TT&C tasks. Firstly, the constraints of satellite missions and ground station resources are analyzed, the constraint satisfaction model based on heuristic rules is established, and the superior initial population is filtered out. And then a modified PSO algorithm combining heuristic rules is designed to solve the resource scheduling problem. The simulation and comparison experiments show that the PSO algorithm has better ability to find excellence and convergence speed than traditional scheduling algorithm (such as genetic algorithm), and that the modified PSO algorithm has better ability to find excellence, convergence speed and stability than the traditional PSO algorithm.
[1] 金光. 卫星地面站测控资源调度CSP模型[J]. 系统工程与电子技术, 2007, 29(7): 1117-1120.DOI:10.3321/j.issn:1001-506X.2007.07.024.
[2] 贺仁杰, 谭跃进. 基于约束满足的卫星地面站资源优化分配问题研究[J]. 计算机工程与应用, 2004, 40(18): 229-232.DOI:10.3321/j.issn:1002-8331.2004.18.072.
[3] 金光, 武小悦, 高卫斌. 卫星地面站资源调度优化模型及启发式算法[J]. 系统工程与电子技术, 2004, 26(12): 1839-1841,1875.DOI:10.3321/j.issn:1001-506X.2004.12.026.
[4] 王远振, 赵坚, 聂成. 多卫星-地面站系统的Petri网模型研究[J]. 空军工程大学学报(自然科学版), 2003, 4(2): 7-11.DOI:10.3969/j.issn.1009-3516.2003.02.002.
[5] Schaire S, Altunc S, Malphrus B K, et al. NASA Wallops flight facility-morehead state ground network for small satellite mission operations[C]//SpaceOps 2014 Conference. 5-9 May 2014, Pasadena, CA. Reston, Virginia: AIAA, 2014.DOI:10.2514/6.2014-1635.
[6] Gooley T D, Borsi J J, Moore J T. Automating air force satellite control network (AFSCN) scheduling[J]. Mathematical and Computer Modelling, 1996, 24(2): 91-101.DOI:10.1016/0895-7177(96)00093-3.
[7] 张鹏, 冯旭祥, 葛小青. 基于改进遗传算法的多天线地面站硬件资源分配方法[J]. 计算机工程与科学, 2017, 39(6): 1155-1163.DOI:10.3969/j.issn.1007-130X.2017.06.020.
[8] 秦玉峰, 聂少军, 赵鸿, 等. 基于高级在轨系统的测控数传一体化方案[J]. 飞行器测控学报, 2016, 35(5): 409-414.DOI:10.7642/j.issn.1674-5620.2016-05-0409-06.
[9] 于志坚, 丁溯泉, 罗伦. 航天测控与数传接收综合信道构想[J]. 遥测遥控, 2002, 23(4): 8-11.
[10] Kennedy J, Eberhart R. Particle swarm optimization [C] // Proceedings of ICNN'95-International Conference on Neural Networks.November 27-December 1,1995, Perth, WA, Australia.IEEE, 1995: 1942-1948.DOI:10.1109/ICNN.1995.488968.
[11] 李爱国, 覃征, 鲍复民, 等. 粒子群优化算法[J]. 计算机工程与应用, 2002, 38(21): 1-3,17.DOI:10.3321/j.issn:1002-8331.2002.21.001.
[12] 杨维, 李歧强. 粒子群优化算法综述[J]. 中国工程科学, 2004, 6(5): 87-94.
[13] Kennedy J, Eberhart R C. A discrete binary version of the particle swarm algorithm [C] // 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational, Cybernetics and Simulation. October 12-25,1997, Orlando, FL, USA. IEEE,1997: 4104-4108.DOI:10.1109/ICSMS.1997.637339.
[14] 常飞. 卫星地面站数传资源配置优化模型与算法研究[D]. 长沙: 国防科学技术大学, 2010.
[15] 唐旻. 卫星系统数传与测控服务策略研究[D]. 长沙: 国防科学技术大学, 2013.
