摘要
为建立高精度的BDS钟差预报模型,提出一种基于改进的萤火虫算法优化的分数阶离散型灰色系统SAFA-FDGM(1,1)钟差预报模型。为避免萤火虫算法陷入局部最优解,提高萤火虫算法的优化能力,本文引入惯性权重因子,同时对吸引力因子、步长因子进行改进;利用改进的萤火虫算法自动优化选取FDGM(1,1)分数阶因子来提高FDGM(1,1)数据拟合精度。分别采用C02(GEO)、C09(IGSO)、C12(MEO)三种不同类型卫星的钟差数据进行实验分析,结果表明,本文预报模型优于传统二次多项式模型与GM(1,1)模型,其中3~6 h预报误差小于1 ns,9~12 h预报误差优于2 ns,对建立高精度的BDS卫星通用钟差预报模型具有重要参考价值。
In order to establish a high-precision BDS clock error prediction model,we propose a SAFA-FDGM(1,1)clock error prediction model based on the fractional-order discrete gray system optimized by the improved firefly algorithm.In order to avoid the firefly algorithm falling into the local optimal solution,and improve the firefly algorithm optimization ability.We introduce the inertia weight factor and improve the attractiveness factor and step factor,and use the improved firefly algorithm to automatically optimize the selection of the fractional-order factor of FDGM(1,1)model to improve the accuracy of FDGM(1,1)data fitting.We apply the clock error data of three different types of satellites,C02(GEO),C09(IGSO)and C12(MEO),for test analysis.The results show that the proposed clock error prediction model is superior to the traditional quadratic polynomial model and the GM(1,1)model,3~6 h prediction error is less than 1 ns,and the 9~12 h prediction error is better than 2 ns,which has important reference value to establish BDS satellite universal clock error prediction model.
作者
袁德宝
张建
张振超
魏盛桃
YUAN Debao;ZHANG Jian;ZHANG Zhenchao;WEI Shengtao(College of Geoscience and Surveying Engineering,China University of Mining and Technology,D11 Xueyuan Road,Beijing 100083,China;College of Geodesy and Geomatics,Shandong University of Science and Technology,579 Qianwangang Road,Qingdao 266590,China)
出处
《大地测量与地球动力学》
CSCD
北大核心
2021年第7期672-675,共4页
Journal of Geodesy and Geodynamics
基金
国家自然科学基金(51474217)
河北省自然科学基金生态智慧矿山联合基金(E2020402086)。
